{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0bbae41c-8ece-45c1-9b10-21c4f0ad8a78",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 导入工具库\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "29c0884b-f701-408d-9197-23366f0b667c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 读取数据\n",
    "df = pd.read_excel('./data/Online_Retail.xlsx')\n",
    "df = df[df['CustomerID'].notna()]\n",
    "# 数据采样\n",
    "df_fix = df.sample(10000, random_state=42)\n",
    "\n",
    "# 只保留日期\n",
    "df_fix[\"InvoiceDate\"] = df_fix[\"InvoiceDate\"].dt.date\n",
    "\n",
    "# 总金额\n",
    "df_fix[\"TotalSum\"] = df_fix[\"Quantity\"] * df_fix[\"UnitPrice\"]\n",
    "\n",
    "# 最近消费时间点快照\n",
    "\n",
    "snapshot_date = max(df_fix.InvoiceDate) + datetime.timedelta(days=1)\n",
    "\n",
    "# 统计聚合\n",
    "customers = df_fix.groupby(['CustomerID']).agg({\n",
    "    'InvoiceDate': lambda x: (snapshot_date - x.max()).days,\n",
    "    'InvoiceNo': 'count',\n",
    "    'TotalSum': 'sum'})\n",
    "# 重命名字段\n",
    "customers.rename(columns={'InvoiceDate': 'Recency',\n",
    "                          'InvoiceNo': 'Frequency',\n",
    "                          'TotalSum': 'MonetaryValue'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fc589c3f-4812-41aa-81be-32ee66634972",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\24211\\AppData\\Local\\Temp\\ipykernel_24268\\187583422.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(customers['Recency'], ax=ax[0])\n",
      "C:\\Users\\24211\\AppData\\Local\\Temp\\ipykernel_24268\\187583422.py:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(customers['Frequency'], ax=ax[1])\n",
      "C:\\Users\\24211\\AppData\\Local\\Temp\\ipykernel_24268\\187583422.py:4: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(customers['MonetaryValue'], ax=ax[2])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 3, figsize=(15, 3))\n",
    "sns.distplot(customers['Recency'], ax=ax[0])\n",
    "sns.distplot(customers['Frequency'], ax=ax[1])\n",
    "sns.distplot(customers['MonetaryValue'], ax=ax[2])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7ea62bea-ed4b-46f8-bdfd-29493bd69266",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\24211\\AppData\\Local\\Temp\\ipykernel_24268\\2182937455.py:4: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(customers[x], ax=ax[0,0])\n",
      "C:\\Users\\24211\\AppData\\Local\\Temp\\ipykernel_24268\\2182937455.py:5: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(np.log(customers[x]), ax=ax[0,1])\n",
      "C:\\Users\\24211\\AppData\\Local\\Temp\\ipykernel_24268\\2182937455.py:6: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(np.sqrt(customers[x]), ax=ax[1,0])\n",
      "C:\\Users\\24211\\AppData\\Local\\Temp\\ipykernel_24268\\2182937455.py:7: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(stats.boxcox(customers[x])[0], ax=ax[1,1])\n"
     ]
    },
    {
     "data": {
      "image/png": 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thBDCBrWaQfzec7he2QwPBwnefmg421XSi4a+iUHcrNME6kBX226vDfNyBJ8HVDbJUNwR0AkhhG3rUq8h9XIFxEI+NsdFws1ewnaV9NJ2flrlKqjU3Fnrw3qg3rBhA0JCQiCVShEZGYljx471Wv7IkSOIjIyEVCpFaGgoNm3a1K3Mvn37MHz4cEgkEgwfPhz79+/vVqakpATPPPMM3NzcYGtrizFjxiAzM9Ng92UsZQ2a+WcfZ5tur9mIBfB30QTwtBs1Jq0XIYTsSi/q9jiQXYrP/tCcH/3RvJGcSW6ij61YAO20OZd61awG6r1792LFihVYtWoVsrKyEBMTg1mzZqGoqEhv+fz8fMyePRsxMTHIysrCm2++iddeew379u3TlUlLS8OCBQsQFxeH7OxsxMXF4fHHH0d6erquTF1dHSZNmgSRSISff/4Zly9fxqeffgpnZ2dj3/JdK+/YJ+3tqD+93SAPzbz1yRvVJqsTIYToU1Lfhv/3XTYA4MUpoZg3zp/lGvWOz+NxcovWgAJ1fn6+QT583bp1WLRoERYvXozw8HAkJycjICAAGzdu1Ft+06ZNCAwMRHJyMsLDw7F48WK88MILWLt2ra5McnIyZsyYgcTERISFhSExMRHTpk1DcnKyrszHH3+MgIAAbN++HePHj0dwcDCmTZuGQYMGGeS+jEWmUKG2RQ4A8OkhgX2oh2ZB2ckbNbRNixiFodo/sWztChV2ny5Cu0KN+4Z54P8eCGO7Sn2iC9QcWvk9oEA9ePBg3Hffffj666/R3j6wrUByuRyZmZmIjY3t8nxsbCxOnjyp9z1paWndys+cORMZGRlQKBS9lul8zR9//BFRUVH485//DE9PT4wdOxZffvllr/WVyWRobGzs8jA17WpvB4kQdhL956kEutpCyOehskmGG1UtpqwesRKGaP/E8v2YXYraFjn8nG2Q/MRYzm3F6gkXF5QNKFBnZ2dj7Nix+Otf/wpvb2+8+OKLOH36dL+uUV1dDZVKBS8vry7Pe3l5oby8XO97ysvL9ZZXKpWorq7utUzna+bl5WHjxo0YMmQIfv31VyxduhSvvfYadu7c2WN9k5KS4OTkpHsEBAT0634NobpZE6jdHXpeiCES8BHoppmnpuFvYgyGaP/Esl2raMK54nrwecD6J8foDrswB1zcojWgQB0REYF169ahpKQE27dvR3l5OSZPnowRI0Zg3bp1qKqq6vO1eLyu37IYhun23J3K3/78na6pVqsxbtw4fPjhhxg7dixefPFFLFmypMchdwBITExEQ0OD7lFcXHznmzOwmo5hbzc7ca/lQt01w9/p+ZROlBieIds/sTxKtRr/O685IGjiIHdEBrmyXKP+0SU9MfcetZZQKMTcuXPx7bff4uOPP8aNGzfw+uuvw9/fHwsXLkRZWc+nObm7u0MgEHTrPVdWVnbrEWt5e3vrLS8UCuHm5tZrmc7X9PHxwfDhXffxhYeH97iIDQAkEgkcHR27PEytpqNHfaetDSHumgVlp/NraZ6aGM3dtH9iudLzalHdLIOdRIj7e0lqom+FOBfYc/BgjrsK1BkZGVi2bBl8fHywbt06vP7667hx4wYOHTqEkpISzJkzp8f3isViREZGIjU1tcvzqampmDhxot73REdHdyt/8OBBREVFQSQS9Vqm8zUnTZqEq1evdilz7do1BAUF3fmmWdTXHrW/iw3EQj6qmmQoqKH91MQ47qb9E8ukVKlxLFczojIj3AtSkYDlGvUfF+eo9a9IuoN169Zh+/btuHr1KmbPno2dO3di9uzZ4PM1cT8kJARffPEFwsJ6X+WXkJCAuLg4REVFITo6Gps3b0ZRURGWLl0KQDPcXFJSops7Xrp0KT777DMkJCRgyZIlSEtLw9atW7F7927dNZcvX44pU6bg448/xpw5c/DDDz/gt99+w/Hjx3Vl4uPjMXHiRHz44Yd4/PHHcfr0aWzevBmbN28eyD+HydQ0dwRq+94DtUjAx5gAZ5zOr8Xp/BpdD5sQQzBU+yeW51xxPRrblXCUCjEuyJnt6gwIF7dnDShQb9y4ES+88AKef/55eHt76y0TGBiIrVu39nqdBQsWoKamBmvWrEFZWRkiIiKQkpKi69mWlZV1GY4OCQlBSkoK4uPj8fnnn8PX1xfr16/HY489piszceJE7NmzB6tXr8Zbb72FQYMGYe/evZgwYYKuzD333IP9+/cjMTERa9asQUhICJKTk/H0008P5J/DJFplSrQpVAAAN7s7Z/WZEOKK0/m1SM+vxYJ7Ao1dPWJFDNX+iWVRqxkcy9UsYJ002B1CPuv5tAbETqwJiy3mHqhTU1MRGBio+watxTAMiouLERgYCLFYjGefffaO11q2bBmWLVum97UdO3Z0e27q1Kk4e/Zsr9ecP38+5s+f32uZhx56CA899NAd68cV1R3D3o5SIcTCOzeA8SGaBRynaUEZMTBDtn9iOdLyalDVLINEyMc9wea1gKwzO4lmuL5NroKaI2t8BvSVZ9CgQbrtUJ3V1tYiJCTkritFuuvrQjKtcYEuEPB5uFnXhpL6NmNWjVgZav9En28zNDthRgc4m+XctJZtR4+agSZYc8GAAnVPK4mbm5shlerPmEXuTl8XkmnZSYSI6DhN6wz1qokBUfsnt2toU+CXi5rdNlFB3M3l3RcCPg9SkSY0cmX4u19D3wkJCQA0+5Tffvtt2NreOsFJpVIhPT0dY8aMMWgFiYa2R+3ej1Nnxoe4IvtmA9Lza/HoWD9jVY1YCWr/pCcHskshU6rh7SiF320HBnFl21V/2ImFaFfI0cKRHnW/AnVWVhYAzTfqCxcuQCy+1bsTi8UYPXo0Xn/9dcPWkAAA6ls1KVJd+tijBoDxIW748lg+TufTSVrk7lH7Jz35MbsUADAu0LnXhFXmwk4iRE2L3Dx71H/88QcA4Pnnn8c///lPVpJ+WKv6Nk2g7k8qvnuCNUNQN6paUN0s61dvnJDbUfsn+lQ2teNMgWZ6LcLPySSfaexeup1YM8feIudGoB7QHPX27dupkZqQSs2gqeMkF+d+BGpnWzHCvB0A0Dw1MRxDtv/+nEf//fffY8aMGfDw8ICjoyOio6Px66+/GqQeZOAOXqoAw2gWkTnb9n3Ej8u0hx61mtvQ97x587Bjxw44Ojpi3rx5vZb9/vvv77pi5JamdgXUDCDg8XRZc/pqfIgrrpQ34XRBLWaN9DFSDYmlM0b7155Hv2HDBkyaNAlffPEFZs2ahcuXLyMwsPve/6NHj2LGjBn48MMP4ezsjO3bt+Phhx9Geno6xo4dO6D7Infv54uaVLGzI/TvqTdH2kBtdkPfTk5OurkHJyfTDG8QjYaOYW9HGyH4/Zz/GR/iip1phbSfmtwVY7T/zufRA5qz5H/99Vds3LgRSUlJ3cp3PlMeAD788EP88MMPOHDgAAVqltS3ynEqT/O3ZVaED45ft4wT+2y1Q9/mFqi3b9+u97+J8WkXkg1kWGl8R+KBy2WNaGxXwFFqPsfNEe4wdPvXnke/cuXKLs/3dh797dRqNZqamuDq2nNyDZlMBplMpvt/Ns6Rt2THcquhUjMY6mWvOV73Ots1MgyuDX0PaI66ra0Nra23DnsoLCxEcnIyDh48aLCKkVsaBrCQTMvTUYoQdzswDJBZUGfoqhErZIj2P5Dz6G/36aefoqWlBY8//niPZbhwjryl2pVehO0nCgAAXg5Ss9yG1ROupREdUKCeM2eO7qCM+vp6jB8/Hp9++inmzJnT65nOZGDq2zTJTvqzkKwz7epvOp+aGIIh239/z6PX2r17N959913s3bsXnp49H6XIhXPkLRXDMMitaAIADPFyYLk2hqVNI8qVfdQDCtRnz55FTEwMAOC7776Dt7c3CgsLsXPnTqxfv96gFSS3hr6dbAcWqMeHaM7qpv3UxBAM0f4Hch691t69e7Fo0SJ8++23mD59eq9luXCOvLnTd270rvQilDe2o0mmhEjAQ7Cb7Z0vZEY696h7ysRnSgMK1K2trXBw0HyDOnjwIObNmwc+n497770XhYWFBq0guTX07WzT9znqLg2qoR2A5gi6HR1DVYQMlCHa/0DOowc0PennnnsOu3btwoMPPjjwmyB37VpFMwBgkIc9hALzPCmrJ9o5aqWa4cQ89YD+dQcPHoz//ve/KC4uxq+//orY2FgAmm/D9I3V8O62R+1iK4KTjQhqBiiua73zGwjphaHaf0JCArZs2YJt27YhJycH8fHx3c6jX7hwoa787t27sXDhQnz66ae49957UV5ejvLycjQ0NBj2BkmfWOqwNwCIBDwI+ZopmNqOcxbYNKBA/fbbb+P1119HcHAwJkyYgOjoaACab9e0TcKwWjqdQz3QOWoe79bQVH51i8HqRqyTodr/ggULkJycjDVr1mDMmDE4evRor+fRf/HFF1AqlXj55Zfh4+OjeyxfvtywN0juSKZQobBG86V/qKc9y7UxPB6Pp+tVcyFQD+g86vnz52Py5MkoKyvD6NGjdc9PmzYNc+fONVjlCFDWoDmiUiLk39XRccHudsi+2YACCtTkLhmy/ffnPPrDhw/3t6rESPKqW6BiGLjaift89K65sZMI0NCmMN9ADQDe3t7w9u6aiWb8+PF3XSHSVUWjZg/oQLZmdRbiZgcAKKpthVyphlhoWXNKxLSo/Vs37cjcIA/L601raReU1ZhroG5pacFHH32E33//HZWVlVCr1V1ez8vLM0jlCHQLwe42UYmHgwS2YgFa5SpcKKlHZFDPSSII6Q21f6IN1CHulrXauzPt0HeduQbqxYsX48iRI4iLi4OPj49FHGvGVRVNHYHaZsCDHwA0cy4h7na4VNqI9PxaCtRkwKj9WzeZQoXSes2UXHDHSJ0l0p6gZbY96p9//hk//fQTJk2aZOj6kNtUdPSoHQyQ+jPYTROoz+TXAn+668sRK0Xt37oV1raCgWY3iaWclqWPrW4xmewOJY1vQBOVLi4uvebXJYajnaN2vMs5akCzoAwAMgrqoFKzv4mfmCdq/9bt1rC35c5PA7fmqGtbFCzXZICB+v3338fbb7/dJd8vMQ7d0Hc/j7fUx8dJComQjyaZEjlldDgBGRhq/9atwArmp4FbaUS50KMe0F//Tz/9FDdu3ICXlxeCg4MhEnXt7Z09e9YglSO3hr4NceoVn8dDkJstrlU043R+LSL86LhS0n/U/q2XXKnGzTrLn58GANuOHnVdK/s96gEF6kcffdTA1SD6qNUMKpsMN/QNaLZpaQP1C5NDDHJNYl2o/Vuv4rpWqBgGjlIhXO0sd34auNWjrmk20x71O++8Y+h6ED1qWuRQqhnwANhL7n7oG7g1T326oBZqNQM+n1bskv6h9m+9tPPTwe52Fr/a376jR93YroRCpYaIxXzmA/7k+vp6bNmyBYmJiait1RyfePbsWZSUlBisctauolEz7G0vEUJgoIDq52IDG5EAtS1yXO3I1UtIf1H7t0635qcte9gbAKRiAbR/deta2d2iNaBu2vnz5zF9+nQ4OTmhoKAAS5YsgaurK/bv36877o7cPW2gdrjLPdSdCfl8TAh1xeGrVTieW41wHzpEhfQPtX/rpFSpUVSrWUAYYuHz04BmTY+tWIAWuQq1LXJ4OkjZq8tA3pSQkIDnnnsOubm5kEpvVX7WrFk4evRov661YcMGhISEQCqVIjIyEseOHeu1/JEjRxAZGQmpVIrQ0FBs2rSpW5l9+/Zh+PDhkEgkGD58OPbv39/j9ZKSksDj8bBixYp+1dsUdFuzDLCQrLPJg90BAMeuVxv0usQ6GLL9E/NRUt8GpZqBnVgADwfLzO99O93BHM3s9qgHFKjPnDmDF198sdvzfn5+3Q6C783evXuxYsUKrFq1CllZWYiJicGsWbO6nJjTWX5+PmbPno2YmBhkZWXhzTffxGuvvYZ9+/bpyqSlpWHBggWIi4tDdnY24uLi8PjjjyM9PV3vfWzevBmjRo3qc51NqbzRcCu+O5s8RBOoT+fXoF3B/lmrxLwYqv0T82JN89Na2pXftSwPfQ8oUEulUjQ2dt+He/XqVXh4ePT5OuvWrcOiRYuwePFihIeHIzk5GQEBAdi4caPe8ps2bUJgYCCSk5MRHh6OxYsX44UXXsDatWt1ZZKTkzFjxgwkJiYiLCwMiYmJmDZtGpKTk7tcq7m5GU8//TS+/PJLuLi49LnOplTZaJj0obcb5uUADwcJ2hVqnC2qM+i1ieUzVPsn5iXfiuantW7tpTbDQD1nzhysWbMGCoVmfxmPx0NRURFWrlyJxx57rE/XkMvlyMzM1B06rxUbG4uTJ0/qfU9aWlq38jNnzkRGRoauLj2Vuf2aL7/8Mh588EFMnz69T/WVyWRobGzs8jA2Y/WoeTyebvj7eC4Nf5P+MUT7J+ZFpWZQqJ2ftqpA3XGCljkOfa9duxZVVVXw9PREW1sbpk6disGDB8PBwQEffPBBn65RXV0NlUoFLy+vLs97eXn1OHxWXl6ut7xSqUR1dXWvZTpfc8+ePcjMzERSUlKf6gpo5rKdnJx0j4CAgD6/d6AMmT70drpATfPUpJ8M0f6JeSlraINcqYZUxIeXI3uLqkxNezCHWa76dnR0xPHjx/HHH38gMzMTarUa48aN63PvtLPb5zoYhul1/kNf+duf7+2axcXFWL58OQ4ePNhlIcydJCYmIiEhQff/jY2NRg/W2qFvBwOkD+1sV3oRGto0vaELNxuw5WgebCVCPDUh0KCfQyyTIds/MQ+6+Wk3O/CtZH4a6NSjZnnou98RQK1WY8eOHfj+++9RUFCgOT4xJATe3t53DLKdubu7QyAQdOs9V1ZWdusRa3l7e+stLxQK4ebm1msZ7TUzMzNRWVmJyMhI3esqlQpHjx7FZ599BplMBoFA0O2zJRIJJBLTrXSUKVW6Xw4nAw99A4CTjQieDhJUNslwo7oFIymdKOkDQ7V/Yl6scX4a6HQwhzkNfTMMg0ceeQSLFy9GSUkJRo4ciREjRqCwsBDPPfcc5s6d2+dricViREZGIjU1tcvzqampmDhxot73REdHdyt/8OBBREVF6fIN91RGe81p06bhwoULOHfunO4RFRWFp59+GufOndMbpNlQ1ZE6VCzkw0ZsnDoN9tScfnO9stko1yeWxZDtn5gPNcOgoOZWj9qa6LZnmVOPeseOHTh69Ch+//133HfffV1eO3ToEB599FHs3LkTCxcu7NP1EhISEBcXh6ioKERHR2Pz5s0oKirC0qVLAWiGm0tKSnQJFJYuXYrPPvsMCQkJWLJkCdLS0rB161bs3r1bd83ly5djypQp+PjjjzFnzhz88MMP+O2333D8+HEAgIODAyIiIrrUw87ODm5ubt2eZ5M22YmXo8RovZTBHvY4eaMG1yspQxm5M0O3f2Ieyhva0a5QQyLkw9fZhu3qmJQu3zfLJ2j1q0e9e/duvPnmm90aKQDcf//9WLlyJb755ps+X2/BggVITk7GmjVrMGbMGBw9ehQpKSkICgoCAJSVlXXZUx0SEoKUlBQcPnwYY8aMwfvvv4/169d3WWk6ceJE7NmzB9u3b8eoUaOwY8cO7N27FxMmTOjPrbJOu5DMy4jZcELc7cDnaU6HYfsbI+E+Q7d/Yh7yOs1PGyqVsbmw79SjVqkZ1urRrx71+fPn8cknn/T4+qxZs7B+/fp+VWDZsmVYtmyZ3td27NjR7bmpU6fe8Ri9+fPnY/78+X2uw+HDh/tc1lTKG7Q9auMFaolIgEBXWxTUtCKXetXkDozR/gn35VdppsasbX4auJXwRM0A9a1yuNmzk5GtXz3q2traHhd6AZptUHV1lEDDEG4NfRt3K8QgmqcmfUTt3/qo1AzyO+anQz2sL1AL+Dw422rWP7E56tivQK1SqSAU9twJFwgEUCqVd10pcivZibeTcb/BDfV0AKAJ1HKl2qifRcwbtX/rk1PWqJuf9nGyrvlpLbeOc7erWVz53a+hb4Zh8Nxzz/W4TUkmY/+AbUvRuUfdIjNePm4/FxvYSYRokSmRUVCLiR2JUAi5HbV/65OerznCNMjN1urmp7Xc7CW4UdXC6oKyfgXqZ5999o5laMWnYWgXk3k7SnGjqsVon8Pn8TDMyx5ni+px6EolBWrSI2r/1udUXg0AINTdnuWasMfdXtOjZjONaL8C9fbt241VD9IJwzBdFpMZM1ADwDBvR02gvlqJ1Q8NN+pnEfNF7d+6qNQMTnf0qK1xIZmWm51mBKmmmb0e9YByfRPjamxXoq3j+ElvJ+Pn1R3iaQ8+D8irakFBtXG/FBBCzMP5m/VoaFNAKrK+/dOduWrnqM1lMRkxDe38tJONCFKR8TOlSUUCXcahQ1cqjf55hBDuO3pNc2DPIA97q52fBm4NfbOZRpQCNQdph729TXhKzTBvzervP65SoCaEAEdzqwAAQzp2hlgr7d5pNheTUaDmIN2KbxMMe2uFeTsC0Cwe0Z6sRQixTg2tCmQVafbED/Gy3oVkwK3tWWazmIyYhi5QO5guC46HgwRDvexxraIZv12uwGOR/ib7bEJI/+1KL+r2nKGOqj1xoxpqBhjkYQcXW7FBrmmutD3qalpMRjq7lezEtAe0zx7pAwD46UKZST+XEMIt2rUqU4d6slwT9mnnqBvblawlhaJAzUHlDR0HcphwjhoAHuwI1Mdyq2j4mxArpVSp8XtOBQBgxvCeU8ZaC0epSLeYjq00ohSoOUg79G3KxWQAMMTLAUO97KFQMUi9XGHSzyaEcMOZgjrUtSrgYivCPcEubFeHdXw+T7dFi60FZRSoOYitoW8AeHCkLwAghYa/CbFKBy+XAwCmhXtBKKAQAbC/oIx+ChyjVKl1ixY8HU1/pNqDo7wBdAx/t9LwNyHWhGEYHLykGU2LpWFvHXeWt2hRoOaYqmYZGAYQ8nlwtzN9oB7s6YAwbwcoVAx+yC4x+ecTQtiTVVyPkvo22IoFiBniwXZ1OMON5XzfFKg5RpvsxNNBAj5L2YAW3BMAANh9uhgMw7BSB2IdNmzYgJCQEEilUkRGRuLYsWM9li0rK8NTTz2FYcOGgc/nY8WKFaarqJX4IUvz5XzmCG/YiI2fFdFcuLJ81CUFao5hI9nJ7eaO9YNYyEdOWSMulDSwVg9i2fbu3YsVK1Zg1apVyMrKQkxMDGbNmoWiou77gwHNMZoeHh5YtWoVRo8ebeLaWj6lSo3/ndesTXlkjC/LteEW3dA3S3upKeEJx7CRPvR2zrZizIrwxg/nSrErvQij/J1ZqwuxXOvWrcOiRYuwePFiAEBycjJ+/fVXbNy4EUlJSd3KBwcH45///CcAYNu2bSatqyW6PWHKtYom1LTI4WYnxmQ67rYL7WIy2p5FAADljezsob7dM/cGAQC+zypBZVM7q3UhlkculyMzMxOxsbFdno+NjcXJkycN9jkymQyNjY1dHkS/sx0pQx8c5QMRrfbuQpedjAI1AToNfbMcqKOCXBAZ5AK5Uo3tJwpYrQuxPNXV1VCpVPDy6rqy2MvLC+Xl5Qb7nKSkJDg5OekeAQEBBru2JWmRKXGpVPMl5vEo+je63a3FZLTqmwAoqW8DAPi5sHv+K4/Hw9KpgwAAX6cV0lYtYhQ8XtcFkwzDdHvubiQmJqKhoUH3KC4uNti1Lcm54nqo1Awi/BwR4efEdnU4R7sDh6183xSoOaakriNQO7PbowaAaWGeGOblgCaZEv86lMt2dYgFcXd3h0Ag6NZ7rqys7NbLvhsSiQSOjo5dHqQrhmFwpqAWALDgHsMc6mFp3B00Pep2hRrNMqXJP58Wk3GISs3ospL5Odua/PP1ncYTPcgNVyua8FVaAZ6+Nwgh7nYmrxexPGKxGJGRkUhNTcXcuXN1z6empmLOnDks1sz6FNe2orJJBpGAh0dG02pvfWzFQjhIhGiSKVHe0I7BnqY9+pN61BxS2dQOlZqBkM+DhwmPuOzNUC8HDPPSJEBZtf8C1GraV00MIyEhAVu2bMG2bduQk5OD+Ph4FBUVYenSpQA0w9YLFy7s8p5z587h3LlzaG5uRlVVFc6dO4fLly+zUX2LcaZAs4gswtcJTjYilmvDXdots5WNpl9cSz1qDtEOe3s7SXWntXDBg6N8UHS4FSdv1GDr8XwsmRLKdpWIBViwYAFqamqwZs0alJWVISIiAikpKQgK0uw4KCsr67aneuzYsbr/zszMxK5duxAUFISCggJTVt1itMqVyL5ZDwC4J9iV3cpwnJejBNcrm1HBwi4YCtQcoltI5szuQrLbudtL8NZDw/Hm/gv45NcriPBzQvQgN7arRSzAsmXLsGzZMr2v7dixo9tzlCnPsDIL66BUM/B2lCLIzfTTbebEy0HTo9YeQ2xKrA999yeFIAAcOXIEkZGRkEqlCA0NxaZNm7qV2bdvH4YPHw6JRILhw4dj//79XV5PSkrCPffcAwcHB3h6euLRRx/F1atXDXpfA8HVQA0AT44PwIOjfKBQMfjLvzNwuZT2oxJiztQMg/R8zSKy6FA3g662t0SeHVtmK1gY+mY1UPc3hWB+fj5mz56NmJgYZGVl4c0338Rrr72Gffv26cqkpaVhwYIFiIuLQ3Z2NuLi4vD4448jPT1dV+bIkSN4+eWXcerUKaSmpkKpVCI2NhYtLS1Gv+felHYEal8OBurdp4sxPtgVwW62aGpXYt7GE/jgpxy2q0UIGaDcimbUtsghFfExOsCZ7epwnnfHaYZsJIBiNVB3TiEYHh6O5ORkBAQEYOPGjXrLb9q0CYGBgUhOTkZ4eDgWL16MF154AWvXrtWVSU5OxowZM5CYmIiwsDAkJiZi2rRpSE5O1pX55Zdf8Nxzz2HEiBEYPXo0tm/fjqKiImRmZhr7lntVWt+x4pvlPdQ9EQn4iLs3GEGutmhXqLHtRD62n8in4UhCzNCpvBoAQGSgC8RC1gdXOU+bhEqb5tmUWPvpDCSFYFpaWrfyM2fOREZGBhQKRa9lektL2NCgOXjC1bXnxRSmSEWoXUzGxR61lo1YgBcmh2CEryNUagbvHbiMJ788hSvlNBROiLkoqmnFtYomAMCEUFpv0he3hr6taI56ICkEy8vL9ZZXKpWorq7utUxP12QYBgkJCZg8eTIiIiJ6rK8pUhGWcniOujORgI+nxgfioVE+kAj5OJVXiwfXH8cb353XNX5CCHd9nV4IBsAQT3vdyVCkd97a7VlN7SYfRWR91Xd/UwjqK3/78/255iuvvILz58/j+PHjvdYzMTERCQkJuv9vbGw0aLBuaFOgqSPjjS8HspLdCY/Hw8RB7njjgTB88FMOfrlUjr0ZxdibUYwJIa6YHu6FyUPcEephB4mw67m2+hKraD01gTIjEWJMje0K7O5og9HUm+4zj44vNAoVg9oWue6gDlNgLVAPJIWgt7e33vJCoRBubm69ltF3zVdffRU//vgjjh49Cn9//17rK5FIIJEY7wdTXNsKQHOcmq2Y9e9PfRbgaotNcZHIKKjFl8fycPByBdLza3WrSfk8wN/FFl6OErjbax6lDW2wlwjhZCNCoKutWd0vIeZuV3oRmmRKeDpIMNTbge3qmA2xkA83OzFqWuSoaJRZR6AeSArB6OhoHDhwoMtzBw8eRFRUFEQika5Mamoq4uPju5SZOHGi7v8ZhsGrr76K/fv34/DhwwgJCTHkrQ1IQY1mxXmwmabojAp2RVSwK27WtSL1cgUOXanE2cI6tMhVKKptRVHHF5Hb8aC55wkhrhjh69Rjb5t62oTcPZlShW3H8wEAMUM8wKctWf3i5SjVBOqmdgyH6fLGs9qVSUhIQFxcHKKiohAdHY3Nmzd3SyFYUlKCnTt3AgCWLl2Kzz77DAkJCViyZAnS0tKwdetW7N69W3fN5cuXY8qUKfj4448xZ84c/PDDD/jtt9+6DG2//PLL2LVrF3744Qc4ODjoeuBOTk6wsWFnfriwRhPIzD3pgL+LLZ6fFILnJ4Xgm1OFaGpXoqZFjmaZEs3tmuH95nYlmmVK1DTLUdUsQ351C/KrW+DpUIlZET4YRt/yCTGKnScLUdkkg4+TFKMD6JSs/vJylOByGVBh4pXfrAbq/qYQDAkJQUpKCuLj4/H555/D19cX69evx2OPPaYrM3HiROzZswerV6/GW2+9hUGDBmHv3r2YMGGCrox2+9ef/vSnLvXZvn07nnvuOePdcC/yqzU96hA38+pR9zbfzOPx4GgjgmMv+YPrWuXILKxD2o0aVDbJ8FVaAcYGOOOhUb6wEQt6fB8hpH/qW+W6U/Dipw+FUk/e/t7aM7m1oKzMmgI10P8UglOnTsXZs2d7veb8+fMxf/78Hl/n4r7fwo6h7yAzHfoeKBdbMaaHe2HSIHf8cbUSJ65XI6u4HtermjFvrD/1rgkxkH+kXkNjuxJh3g54LNIfe8/Q2dz95e+iGfG82bGV1lRYD9REo6Bj6DvYzIe+B8pGLMDskT6I8HXEd2dLUN2s6V2PD3HF7AgftqtHCOeoGQaXShtxrqgOta1y2ImFaJEpMWeMr27Pr9ZvlyvwVVohAGD1g8M5deiPOfHvSEZV3MOaG2OhQM0BLTIlqpo0m+iDXK2rR327QDc7vHr/YPxyqRxpN2pwOr8W1yub4edig+nhnpSPmBBotlh9faoQV8o75y2Q4YOUHHzy6xU8PMoXz04MxhAve/x0vgxv/XARAPBsdBAmD3Fnp9IWINBV05EqrqNAbXW0K75dbEVwsqXzYEUCPh4e5Ytwb0fsO3sTtS1yLNmZgdH+ToiLDsa0ME+42InZriYhrKhrkePxL9KQW9kMIZ+HyUPcEeJmh+oWOUrqWnG2qB7fZ5Xg+6ySLu+bFuaJ1Q8NZ6nWliGgI1CXN7ZDplR1yxFhLBSoOUC74ttct2YZy2BPe6yYNgSHr1UhLa8G2TcbkP2fbPB4mm+2AS62cJAKYScRwlYsgI1YAFuREK72YgS72WKQhz18nKTUCycWo02uwgtfnUFuZTMcpUI8c2+Qbt50CICnJoxFdnE9th7Px285FWiVq+BuL8Hzk4Lx4pRQCAWU0/tuaPJcCNAqV6Gkrg2hHvYm+VwK1Byg20NtZiu+TUEiEmDmCG98+vho7EovQsqFMlwpb0JhTavuC05vvB2liAp2wX3DPHE/9cSJGWMYBiu/P4+sono42Yjw/MTgbnPRADA6wBnrnxwLlZpBi1wJB4mQvqwaCI/HQ4CLLa5WNKGotpUCtTXJq+pY8W2lC8n6wt1egtemDcFr04agulmGaxVNqGyUoaldgeO51ZCr1JAr1ZCrGDS1K1DTIkdtixzlje343/ky/O98GQR8Hu4JdsGjY/zw4CgfOEhpmoGYj12ni/DDuVII+Dx8ERep+7vREwGfB0f6HTe4AFdNoC424cpvCtQccLVjQUgYbUXqE20qUi0BX/9wnlypxs26VtyoakFOWSPKG9txKq8Wp/Jq8e6BS5gd4YPnJ4VgpP/dJX6g3OVkIPT93vT0+3KxpAHv/XgZAPB/M4fh3lC3OwZqYhwBrqZf+U2BmmVKlVp34lSYt+lS0lkDsZCPUA97hHrYY8ZwL9S2yHGhpAFnC+tQ1SzTLbiZNNgN8dOHIiq452NOCWHL1mP5+OyPXMhVaoR5O8BOIqTEJCzSrfymQG09CmpaIVOqYSMS6H4BiHG42okxdagHpgxxx3BfR+xMK8SP2aU4cb0GJ66n4eHRvkicFcbp88CJdVGrGXybUYy6VgVcbEX4c2QA5edmWUDH4r2ezi8wBgrULNMOew/1dgCfkhD0yJA9CB6Ph5yyJtwT7IrBnvb440olMgvrcCC7FH9cqcS7j4zAY+P8aAEOYd1nf1zH1YomCPk8PD0hiNLqckCg261AfadjmQ2FAjXLrpQ3AgDCaX6aFS62Yswb5497Q93wY3Ypimpb8fp/srHjRD7mjvXv8oeR5puJKf1wrgTrUq8BAOaM8aORHo4IdLUFnwc0tStR3SyHh4Pxj7ukTXUsyymjhWRc4Otsg79MCUXscC/wecDF0kZ89kcubpo4AxEhAHDoSgVe/082AGDSIDdEBrmwXCOiJe00TZlb0XSH0oZBPWqWaXvUYT60kIxtfB4PfxrmicGe9th9ugh1rQp8cTQPsyO8cW+oG9vVI1Zif9ZNvP6f81CpGTw0yueuf/do4ZnhDfZ0QEFNK3IrmzFxsPFTslKPmkWN7QrdKSzUo+YOfxdbvHLfEAz3cYRKzeDA+TLsPlOMxnYF21UjFm7b8XzE782GSs1g7lg//GPBGFo8xkFDvDSJTnIrTdOjpkDNouziegCafXnOtpQxi0tsxAI8PSEQD4700QyFlzTg4X8dx4nr1WxXjVgghmGQerkca/6n2Sv9/KRgfPrn0RBRyk9OGtoRqK+UUaC2eGcL6wEA4wJp/omLeDweJg12x4tTBsHZRoTCmlY8vSUdz247jcNXK6FQqdmuIrEADMPg54vl+ONqFQDg9dihePuh4bQLhMNG+GqSJF0ua4RazRj982iOmkVni+oAUKDmugBXW7x6/xAU17Xi36cKceRaFY5cq4KDRIiYoe4QCfgIdbeHu72YtnSRfmEYBj9dKMPJGzUAgDVzRmBhdPCAr0fz0aYxyMMeUhEfrXIV8mtaMMjIOb8pULNEqVIjs1ATqGlFJ/fZiAV495EReH5SMLafKMCB7FLUtMiRcqFcV8ZBKsQYf2dMCHWDKx3+Qfrg9yuVuiD96Bi/uwrSxHQEfB6G+zjibFE9LpY0GD1Q09A3S7JvNqBZpoSzrQjDacW32Qhys8O7j4zA6VXTsX/ZRKyYPgQh7nYQ8nloalfi2PVq/CP1Gn65WAa5kobGSc8ulDTg0JVKAMCcMb4YH0IpbM3JKH9nAEBWUb3RP4t61CzRLkqKDnWjuSgzJODzMDbQBWMDXeDpIIVCpcb1ymak3ajB9apmHM2txrWKZtwX5oEgOr6U3OZiSQO+yywGoNknPSGEtv+Zm3uCXbHjZAHS82uN/lnUo2bJ7zkVAIApQz1YrgkxBJGAj3AfR7wwOQRx9wbBTiJEeWM7Hv38BC6VNrBdPcIhlU3tWLIzAwoVg6Fe9nggwoftKpEBuCdEM2V5pbwRDa3G3bpJgZoF5Q3tyL7ZAB4PmBbuyXZ1iIGF+zjilfsGw8/ZBnWtCjz1ZTou3KRgTYB2hQp/2ZmJsoZ2uNtLsCAqEAIaUTNLng5ShLrbgWGAkzeMu22Thr5Z8L/zpQCAsQHO8HSQslwb0lf9WVHrZCPCoskhOHC+FFlF9XhqyynsWnzvXZ99TcwXwzB4Y995nCuuh5ONCAuj6ZANc3dfmCfyjufj9yuVmDXSeCMjFKhNjGEY/CfjJgBg3jh/lmtDjEkqEuDfiybg+e2ncaagDs9sTcfuJfdiuC8tHrRUPX2Ze3J8AD7+5Sp+OFcKIZ+Hjc+MQ0E15ZE3d9PCPbH1eD4OXdHkVTBWghoa+jaxtBs1uFrRBImQj4dH+7JdHWJk9hIhtj8/HuMCndHQpsAzW9N1R5sS66BUqbH6vxex6cgNAMCaORGYOMj4+aGJ8Y0PdoW7vQS1LXL80bGC3xgoUJsQwzD416HrAIAF9wTAyUbEco2IKdhLhNjxwniM8ndCbYscT285hesmyhFM2KNQqXH+Zj3+deg6vkkvAo8H/O3RCDou1YIIBXzMG+cHAPjGiMlmaOjbhH65WI60vBqIhXwsiQlluzrEhBylIvz7hQl4asspXCptxIIvTmHzwkhEBtHeWUtQ1yJHRmEdfsupQGVjOyqbZKhulkGbXdLdXoIP50YgdoQ3uxUlBvf0hEBsPZ6PI9eqcK64HmMCnA3+Gaz3qDds2ICQkBBIpVJERkbi2LFjvZY/cuQIIiMjIZVKERoaik2bNnUrs2/fPgwfPhwSiQTDhw/H/v377/pz71ZRTSve3H8BAPCXmFAEdJxnSqyHk60IXy+agAg/R9S0yPHk5nRsP5FvklzBXGWM9m8qFY3t+PJoHh761zGMfT8VS3Zm4NCVSlwsbURlkyZIO9mIcN8wD/yWMIWCtIUKcrPDo2M0verV/71glERHrAbqvXv3YsWKFVi1ahWysrIQExODWbNmoahI/xBCfn4+Zs+ejZiYGGRlZeHNN9/Ea6+9hn379unKpKWlYcGCBYiLi0N2djbi4uLw+OOPIz09fcCfe7culjTgyS9Poa5VgZF+Tnh12mCjfA7hPhc7Mb59MRqxw70gV6nx3oHLeGzTSZy4Xg2Gsa6AbYz2b2ztChV+zC7Fwm2nEZ30Oz5IycHFEs2Z8oM87BAZ6ILZEd54bmIw/m/mMPzfzGGYMdybTsezcG/MGgYnGxEuljTi9f9kGzxY8xgW/zpMmDAB48aNw8aNG3XPhYeH49FHH0VSUlK38m+88QZ+/PFH5OTk6J5bunQpsrOzkZaWBgBYsGABGhsb8fPPP+vKPPDAA3BxccHu3bsH9Ln6NDY2wsnJCQ0NDXB07LqKl2EYlDW0I6uoHgeyS3HwcjnUDBDqboc9f7kXno7925JFifbNV0/zkQzD4Ov0Inz4Uw7aFCoAQLCbLaaHe2FMoDPCfRzh7SiFneTOs1O9/S5ymTHa/530599KoVKjtkWOG1XNuFLWhBPXq3EqrwYtcpWuTGSQCx4d64dZEd5wt5f02Fb1/R7oK9vXcsR0+rqm4PDVSiz6KgMqtSaRTVx0MO4P84Svk1TvYT39+V1kbY5aLpcjMzMTK1eu7PJ8bGwsTp48qfc9aWlpiI2N7fLczJkzsXXrVigUCohEIqSlpSE+Pr5bmeTk5AF/LgDIZDLIZDLd/zc0aBJYNDY2div70c9X8PWpwq51GO6Ftx4eDinkaGyU9/g5+rS20MIjc6Xv90NrznAXRPuPw5Zjefg+qwR5pa3YXNo1cYJExIedSACJSIA5o33xyrQhPX6GOfXIjdX+b9efdrv/7E1sOZ6P5nYlmuVKyBT6e0U+TlLMGe2Lh8f43koPq5ahsVHWY1vV93n6yva1HDGd3tpwZ+N8pPjnY8Pw5r7zuFJUiVVFmlXgU4e64/OnI3u8bl/aLWuBurq6GiqVCl5eXl2e9/LyQnl5ud73lJeX6y2vVCpRXV0NHx+fHstorzmQzwWApKQkvPfee92eDwgI6PkmO9nS8SDWZYkBr3UawKpeXm9qaoKTk3kkVDFW+7/d3bZbfYpx55/F7fr6e2DI3xdiGHf7M/kawNfLen69L+2W9VXftw8JMAzT65m++srf/nxfrtnfz01MTERCQoLu/9VqNWpra+Hm5mbUM4gbGxsREBCA4uJisxrWHAi614FhGAZNTU3w9TW/ffnGaP+dmbLdWtPvL9eZw8+iP+2WtUDt7u4OgUDQ7dtzZWVlt2/NWt7e3nrLC4VCuLm59VpGe82BfC4ASCQSSCSSLs85Ozv3fIMG5ujoyNlfOEOje+0/c+lJaxmr/d+OjXZrTb+/XMf1n0Vf2y1rq77FYjEiIyORmpra5fnU1FRMnDhR73uio6O7lT948CCioqJ081M9ldFecyCfSwgxLGO1f0IsEsOiPXv2MCKRiNm6dStz+fJlZsWKFYydnR1TUFDAMAzDrFy5komLi9OVz8vLY2xtbZn4+Hjm8uXLzNatWxmRSMR89913ujInTpxgBAIB89FHHzE5OTnMRx99xAiFQubUqVN9/lwuaWhoYAAwDQ0NbFfF6OherYsx2j+b6GfKHZb2s2A1UDMMw3z++edMUFAQIxaLmXHjxjFHjhzRvfbss88yU6dO7VL+8OHDzNixYxmxWMwEBwczGzdu7HbN//znP8ywYcMYkUjEhIWFMfv27evX53JJe3s788477zDt7e1sV8Xo6F6tjzHaP1voZ8odlvazYHUfNSGEEEJ6x3oKUUIIIYT0jAI1IYQQwmEUqAkhhBAOo0BNCCGEcBgFag4z9VGcxpCUlIR77rkHDg4O8PT0xKOPPoqrV692KcMwDN599134+vrCxsYGf/rTn3Dp0qUuZWQyGV599VW4u7vDzs4OjzzyCG7evGnKW+m3pKQk8Hg8rFixQvecpd4rsYz2as7effdd8Hi8Lg9vb8s4WpQCNUeZ+ihOYzly5AhefvllnDp1CqmpqVAqlYiNjUVLS4uuzCeffIJ169bhs88+w5kzZ+Dt7Y0ZM2agqenWYQQrVqzA/v37sWfPHhw/fhzNzc146KGHoFKp9H0s686cOYPNmzdj1KhRXZ63xHslltNezd2IESNQVlame1y4cIHtKhkGu7vDSE/Gjx/PLF26tMtzYWFhzMqVK1mqkWFUVlYyAHT7ZdVqNePt7c189NFHujLt7e2Mk5MTs2nTJoZhGKa+vp4RiUTMnj17dGVKSkoYPp/P/PLLL6a9gT5oampihgwZwqSmpjJTp05lli9fzjCMZd4r0bDU9mpO3nnnHWb06NFsV8MoqEfNQdojAG8/0u9OR3GaA+0xg66urgCA/Px8lJeXd7lXiUSCqVOn6u41MzMTCoWiSxlfX19ERERw8t/j5ZdfxoMPPojp06d3ed4S75VYdns1N7m5ufD19UVISAieeOIJ5OXlsV0lg2D99CzS3UCP4uQ6hmGQkJCAyZMnIyIiAgB096PvXgsLC3VlxGIxXFxcupXh2r/Hnj17kJmZiYyMjG6vWdq9Eg1Lba/mZsKECdi5cyeGDh2KiooK/O1vf8PEiRNx6dKlHg9tMRcUqDmsv0cAct0rr7yC8+fP4/jx491eG8i9cu3fo7i4GMuXL8fBgwchlUp7LGcJ90q6s7T2am5mzZql+++RI0ciOjoagwYNwldffdXlqFNzREPfHDTQozi57NVXX8WPP/6IP/74A/7+/rrntasye7tXb29vyOVy1NXV9ViGCzIzM1FZWYnIyEgIhUIIhUIcOXIE69evh1Ao1NXVEu6V3GKJ7dUS2NnZYeTIkcjNzWW7KneNAjUHWdJRnAzD4JVXXsH333+PQ4cOISQkpMvrISEh8Pb27nKvcrkcR44c0d1rZGQkRCJRlzJlZWW4ePEip/49pk2bhgsXLuDcuXO6R1RUFJ5++mmcO3cOoaGhFnOv5BZLaq+WRCaTIScnBz4+PmxX5e6xt46N9MacjuLszUsvvcQ4OTkxhw8fZsrKynSP1tZWXZmPPvqIcXJyYr7//nvmwoULzJNPPsn4+PgwjY2NujJLly5l/P39md9++405e/Ysc//99zOjR49mlEolG7fVZ51XfTOMZd+rNbOU9mrO/vrXvzKHDx9m8vLymFOnTjEPPfQQ4+DgYBE/AwrUHGYuR3H2BoDex/bt23Vl1Go188477zDe3t6MRCJhpkyZwly4cKHLddra2phXXnmFcXV1ZWxsbJiHHnqIKSoqMvHd9N/tgdqS79XaWUJ7NWcLFixgfHx8GJFIxPj6+jLz5s1jLl26xHa1DIKOuSSEEEI4jOaoCSGEEA6jQE0IIYRwGAVqQgghhMMoUBNCCCEcRoGaEEII4TAK1IQQQgiHUaAmhBBCOIwCNSGEEMJhFKgJIYQQDqNATbp57rnnwOPxwOPxIBQKERgYiJdeeqnbiU6EEO6gdmu5KFATvR544AGUlZWhoKAAW7ZswYEDB7Bs2TK2q0UI6QW1W8tEgZroJZFI4O3tDX9/f8TGxmLBggU4ePCg7vXt27cjPDwcUqkUYWFh2LBhQ5f337x5E0888QRcXV1hZ2eHqKgopKen614/cOAAIiMjIZVKERoaivfeew9KpVL3Oo/Hw5YtWzB37lzY2tpiyJAh+PHHH7t8xqVLl/Dggw/C0dERDg4OiImJwY0bN3D06FGIRKJu5wP/9a9/xZQpUwz5z0QIp1C7tVBsnwpCuOfZZ59l5syZo/v/GzduMMOHD2e8vLwYhmGYzZs3Mz4+Psy+ffuYvLw8Zt++fYyrqyuzY8cOhmEYpqmpiQkNDWViYmKYY8eOMbm5uczevXuZkydPMgzDML/88gvj6OjI7Nixg7lx4wZz8OBBJjg4mHn33Xd1nwmA8ff3Z3bt2sXk5uYyr732GmNvb8/U1NQwDMMwN2/eZFxdXZl58+YxZ86cYa5evcps27aNuXLlCsMwDDN06FDmk08+0V1PoVAwnp6ezLZt24z6b0cIW6jdWi4K1KSbZ599lhEIBIydnR0jlUp1R1OuW7eOYRiGCQgIYHbt2tXlPe+//z4THR3NMAzDfPHFF4yDg4Oucd4uJiaG+fDDD7s89+9//5vx8fHR/T8AZvXq1br/b25uZng8HvPzzz8zDMMwiYmJTEhICCOXy/V+xscff8yEh4fr/v+///0vY29vzzQ3N/f1n4EQs0Lt1nJRoCbdPPvss8z06dOZ3NxcJjs7m3n11VeZmTNnMgqFgqmsrGQAMDY2NoydnZ3uIZFIGE9PT4ZhGOall15ipkyZ0uP1bW1tGalU2uX92j8sLS0tDMNoGvy3337b5X2Ojo7MV199xTAMw8yaNYtZuHBhj59RUVHBiEQiJi0tjWEYhnnkkUeYF1544a7+XQjhMmq3lkvIxnA74T47OzsMHjwYALB+/Xrcd999eO+99/DKK68AAL788ktMmDChy3sEAgEAwMbGptdrq9VqvPfee5g3b16316RSqe6/RSJRl9d4PB7UanWfPsPT0xMPP/wwtm/fjtDQUKSkpODw4cO9vocQc0ft1jJRoCZ98s4772DWrFl46aWX4Ofnh7y8PDz99NN6y44aNQpbtmxBbW0tXF1du70+btw4XL16VfcHZSBGjRqFr776CgqFotsfBq3FixfjiSeegL+/PwYNGoRJkyYN+PMIMUfUbi0E2116wj23L0rRioyMZF5++WXmyy+/ZGxsbJjk5GTm6tWrzPnz55lt27Yxn376KcMwDCOTyZihQ4cyMTExzPHjx5kbN24w3333XZdFKUKhkHnnnXeYixcvMpcvX2b27NnDrFq1SvdZAJj9+/d3+XwnJydm+/btDMMwTHV1NePm5qZblHLt2jVm586dukUpDMMwKpWKCQgIYMRiMfPRRx8Z9h+JEI6hdmu5KFCTbnpq8N988w0jFouZoqIi5ptvvmHGjBnDiMVixsXFhZkyZQrz/fff68oWFBQwjz32GOPo6MjY2toyUVFRTHp6uu71X375hZk4cSJjY2PDODo6MuPHj2c2b96se/1ODZ5hGCY7O5uJjY1lbG1tGQcHByYmJoa5ceNGl/e89dZbjEAgYEpLS+/uH4UQjqN2a7l4DMMw7PbpCTGeJUuWoKKiotteTkIId1G77YrmqIlFamhowJkzZ/DNN9/ghx9+YLs6hJA+oHarHwVqYpHmzJmD06dP48UXX8SMGTPYrg4hpA+o3epHQ9+EEEIIh1Gub0IIIYTDKFATQgghHEaBmhBCCOEwCtSEEEIIh1GgJoQQQjiMAjUhhBDCYRSoCSGEEA6jQE0IIYRwGAVqQgghhMMoUBNCCCEcRoGaEEII4TAK1IQQQgiH0elZA6RWq1FaWgoHBwfweDy2q0OsGMMwaGpqgq+vL/h8+u7dG2q3hCv6024pUA9QaWkpAgIC2K4GITrFxcXw9/dnuxqcRu2WcE1f2i0F6gFycHAAoPlHdnR0ZLk2xJo1NjYiICBA9ztJekbtlnBFf9otBeoB0g6bOTo6UoMnnEBDuXdG7ZZwTV/aLU1oEUIIIRxGgZoQQgjhMArUhBBCCIdRoCaEEEI4jAI1IYQQwmEUqAkhhBAOo+1ZVmpXelGPrz01IdCENSGEsK2nvwf0t4AbKFATQggxGAr6hkdD34QQo9iwYQNCQkIglUoRGRmJY8eO9Vi2rKwMTz31FIYNGwY+n48VK1boLbdv3z4MHz4cEokEw4cPx/79+41Ue0K4gwI1IcTg9u7dixUrVmDVqlXIyspCTEwMZs2ahaIi/b0tmUwGDw8PrFq1CqNHj9ZbJi0tDQsWLEBcXByys7MRFxeHxx9/HOnp6ca8FUJYR4GaEGJw69atw6JFi7B48WKEh4cjOTkZAQEB2Lhxo97ywcHB+Oc//4mFCxfCyclJb5nk5GTMmDEDiYmJCAsLQ2JiIqZNm4bk5GQj3gkh7KM5akKIQcnlcmRmZmLlypVdno+NjcXJkycHfN20tDTEx8d3eW7mzJm9BmqZTAaZTKb7/8bGxgF/PumqtwWpxLCoR00IMajq6mqoVCp4eXl1ed7Lywvl5eUDvm55eXm/r5mUlAQnJyfdg464JOaIAjUhxChuPxWIYZi7PuGrv9dMTExEQ0OD7lFcXHxXn08IG2jomxBiUO7u7hAIBN16upWVld16xP3h7e3d72tKJBJIJJIBfyYhXEA9akKIQYnFYkRGRiI1NbXL86mpqZg4ceKArxsdHd3tmgcPHryra5KBU6rU+ONqJf7+6xWs+d8l7EovRH2rnO1qWSTqURNCDC4hIQFxcXGIiopCdHQ0Nm/ejKKiIixduhSAZki6pKQEO3fu1L3n3LlzAIDm5mZUVVXh3LlzEIvFGD58OABg+fLlmDJlCj7++GPMmTMHP/zwA3777TccP37c5Pdn7ZraFdh+sgD51S265y6WNqKothUv/WkwnGxELNbO8phFj7o/iRMA4MiRI4iMjIRUKkVoaCg2bdrUrUxycjKGDRsGGxsbBAQEID4+Hu3t7ca6BUKsyoIFC5CcnIw1a9ZgzJgxOHr0KFJSUhAUFARAk+Dk9j3VY8eOxdixY5GZmYldu3Zh7NixmD17tu71iRMnYs+ePdi+fTtGjRqFHTt2YO/evZgwYYJJ783a1bbI8fSWdORXt0Ai5GN+pD9enBIKD3sJGtuV+DajGAzDsF1Ni8L5HrU2ccKGDRswadIkfPHFF5g1axYuX76MwMDuKeny8/Mxe/ZsLFmyBF9//TVOnDiBZcuWwcPDA4899hgA4JtvvsHKlSuxbds2TJw4EdeuXcNzzz0HAPjHP/5hytsjxGItW7YMy5Yt0/vajh07uj3Xlz/u8+fPx/z58++2amSAyhraELf1NK5XNsNWLMDzE0Pg52IDAIiLDsK/DuUiv7oFl0obEeGnfz886T/O96j7mzhh06ZNCAwMRHJyMsLDw7F48WK88MILWLt2ra5MWloaJk2ahKeeegrBwcGIjY3Fk08+iYyMDFPdFufIlWrkV7egrKGNvg0TQrq5cLMB8zem4XplM3ycpPjLlFBdkAYAd3sJJg92BwAculJJf0cMiNM96oEkTkhLS0NsbGyX52bOnImtW7dCoVBAJBJh8uTJ+Prrr3H69GmMHz8eeXl5SElJwbPPPttjXSw5ccKNqmZ8e6YYTTIlACDtRg2SnxgDfxdblmtGCDGFisZ2nC2sQ1WzDBIhH862Yng7SRHh5wiZUo3fciqw9Vg+lGoGIe52+Pei8Th6rbrbdSYP9sDx69Uob2xHXnULBnnYs3A3lofTgXogiRN6SoqgVCpRXV0NHx8fPPHEE6iqqsLkyZPBMAyUSiVeeumlbl8IOktKSsJ777139zfFMeUN7diZVgCFioGtWAC5Uo2MwjrM/ucxvYtC6AQcQizLlmN5+NehXKj1dID3num673xWhDeS5o2Es61Y77VsxAKMDXDB6YJanC2so0BtIJwO1Fr9TXKgr3zn5w8fPowPPvgAGzZswIQJE3D9+nUsX74cPj4+eOutt/ReMzExEQkJCbr/b2xsNPssR2qGwfdZN6FQMRjsYY+46CA0y5TYcaIAVc0y7EovxJKYUAgFnJ8hIYQMwNenCvG3n3IAAGHeDhjm7QC5Uo2aZjnKGtogV6khFvIxzMsBC+4JxPRwzzsmrRkb6IzTBbW4XNYIhUoNEf39uGucDtQDSZzQU1IEoVAINzc3AMBbb72FuLg4LF68GAAwcuRItLS04C9/+QtWrVoFPr/7L5YlJk64VNqIm3VtmpWbUf4QCfhwsRVjYXQQPj98HcV1bfj5UjkeHuXLdlUJIQZ2vbIZaw5cBgBMC/PEtPDuf1MHMoIW4GoLZxsR6tsUuFLehJG0qOyucfqrzkASJ/SUFCEqKgoikWYYt7W1tVswFggEYBjGqhZAHL1WBQCYNNgdjtJbQ9xu9hI8HqUZLUi7UYMbVc2s1I8QYjxr/ncZcpUafxrmgfvDPA12XT6Ph1H+muB8/ma9wa5rzTgdqAFN4oQtW7Zg27ZtyMnJQXx8fLfECQsXLtSVX7p0KQoLC5GQkICcnBxs27YNW7duxeuvv64r8/DDD2Pjxo3Ys2cP8vPzkZqairfeeguPPPIIBAKBye+RDRWN7SipbwOfB9wb6tbt9TBvR4wPdgUA7Dt7EzKFytRVJIQYydmiOhy9VgUhn4f3Hhlx1znYbzfK3xkAcLW8CXKl2qDXtkacHvoGNIkTampqsGbNGpSVlSEiIqLXxAkhISFISUlBfHw8Pv/8c/j6+mL9+vW6PdQAsHr1avB4PKxevRolJSXw8PDAww8/jA8++MDk98eWc8X1AIBhXg6wl+j/NZgV4Y3cyibUtSqQcrEMc8f6m7CGhBBD0x5NuSu9EAAw2t8ZJ67X3LF8f/k4SXXD33nVzQjzdhzQdYgGj7GmsV4DamxshJOTExoaGuDoaF6/hGo1g3Hvp6K+TYEnxwf2OoeUV9WMLcfzAQDPTQzGu4+MMFU1SR+Z8++iqVn7v9Wu9CK0ypVI+vkKVGoGr94/GD5ONnd+4wD891wJTufXYkKIK+aM8aMdI7fpz+8i54e+ieFlFNahvk0BiZCPMG+HXsuGetgjepBmaPz7szfR0KYwRRUJIUZyoaQBKjUDHyep0YI0AIR5af62XK1osqq1P8bA+aFvYniHrlQCAMJ9HPu0dWLmcG9cK29CTYsc7x24hHWPjzFyDQkhxnK2sA4AMDbA2aifE+phDyGfh/pWBSqbZHd+Qz/0NCRvqb126lFbIe1q76FefUtGIO5IvM8D8P3ZEvw7rcB4lSOEGE1tixzFdW3gARht5EAtFvIR6mEHALhW0WTUz7J0FKitTFWTDJfLNOlPB3v2PuzdWZCbHWYM1+yzfOfHS/jlov7McIQQ7rparmn7we52cJAa/yjKIR1/Y3IraIvn3aChbytzLFfTm/Z1lva42rsnU4d6wMVWjL0ZxXh511l8NG8k/hx1KztbbytELXVIihBzcq0jYA716vuX9LsxxMseuAAU1LSgTa6CjZi97a/mPFxOPWorox32HtKP3rQWj8fDB3MjMD/SHyo1g//33Xl8eTTP0FUkhBhBu0KFvGptoDZNDm4PewmcbURQqhmcyu95GxjpHQVqK3MqrxYAMNhzYA1VKODjk8dGYUlMCADgg5QcfPzLFVrVSQjHnc6vhULFwFEqhLej1CSfyePxNL1q3OokkP6jQG1FyhraUN7YDgGfh4C7OMKSz+fhzdnh+L8HhgEANh6+gVX/vUjBmhAOO6IdTfNyMHgmst5oR+8oUA8cBWorcq6oHoAmG5lYeHc/eh6Ph2V/GoykeSPB52nmf47ldj+flhDCDafyNEPPAx1NG6hBHvbg84AbVS0oqW8z6WdbCgrUViSrI23o2EBng13zyfGBeK8jW9mvl8rpAA9COKipXYGcjt0ewW52Jv1sG7EA/h0jeNSrHhha9W3Bbl/l+OslzZYqmcKwSfKfuTcI52824D+ZN/HfrBIsnz4EQj1HhRJC2JFVVA81A7jYiuBkY/xtWbcb4mWPotpWHL1WhSfHG2aVNcMwyKtuwY3KZlS3yCEW8FDdLMN9wzwR4edo0uF9Y6NAbSVUagYldZphJ3/XgacN7GmLQ7iPI+wlQtS0yJF2owYxQzwG/BmEEMM6U6BZRGrq3rTWUE8H/J5TiePXq6FUqSHsQ0bE3tS1yPFtRjEKa1u7PH+2qB7rUq8hws8Rr8cOw5+GGe74TjZRt8dKlDe2Q6lmIBXx4W4vMfj1pSIBZo7QJEQ5dKUS7XQsJiGcoQ3UQSwFaj8XGzjbitDUrtSd3DdQZ4vqsOHwdRTWtkIk4GFcoAtmj/TBzOFeeGCEN2xEAlwsacRz289g1f4LUKrM/5hNCtRWorjjm2eAiy34RhoSGhvoAk8HCWRKNTI68gkTQtglV6p1wTHYbeC7Pe4Gn8fD5MHuAIDfcioHfJ1rFU14dutptMhV8HGSIn76UMyP9Mfkwe6YOswTm+IicfyN+/DCpBDweMA36UWI/zYbarV570ihQG0ldIHa1XgNlc/jYeIgTWM8lVcDNW3XsmobNmxASEgIpFIpIiMjcezYsV7LHzlyBJGRkZBKpQgNDcWmTZu6lUlOTsawYcNgY2ODgIAAxMfHo7293Vi3YJZ2pRd1efzzt1y0K9SwEQng7mD40bS+mjnCGwDwy8WyAW3lrGmW4fntZ9AkUyLI1RZ/mRIKZ1txt3Ju9hK8/fBwfPFMJEQCHg5kl2LT0Rt3XX82UaC2EkUdgTrQiIEaAMYEOMNGJEBtixxXyykRv7Xau3cvVqxYgVWrViErKwsxMTGYNWsWior0r3HIz8/H7NmzERMTg6ysLLz55pt47bXXsG/fPl2Zb775BitXrsQ777yDnJwcbN26FXv37kViYqKpbssslTVo1qb4OkuNNprWF/eFeUIs5KOgphVX+3lIB8MwSPz+Akrq2xDsZotn7g2CRNh7OtLYEd54f04EAGDdwWuobDTfL3QUqK1Aq0yJmhY5AMDfxXjnzwKaE3Oigl0AAJk0/G211q1bh0WLFmHx4sUIDw9HcnIyAgICsHHjRr3lN23ahMDAQCQnJyM8PByLFy/GCy+8gLVr1+rKpKWlYdKkSXjqqacQHByM2NhYPPnkk8jIyDDVbZml0o5Abcyzp/vCXiLElI5Fpj9f6N+hPv/JuImDlysgEvDw+dPjYNfHcwoW3BOAaWGeUKoZ/GzGBwlRoLYCxR2rvd3txbAVG3+h/9gATaC+WtFEi8qskFwuR2ZmJmJjY7s8Hxsbi5MnT+p9T1paWrfyM2fOREZGBhQKBQBg8uTJyMzMxOnTpwEAeXl5SElJwYMPPthjXWQyGRobG7s8rE1pvaYn6etsmrShvXkgQjP8nXKh78PfRTWteO/AJQBAwoxhGOHr1OfP4/F4WPVgOPg8zd+jUjNNuEKB2goU191aSGYK3k5SeDpIoFIzuFxqfX8YrV11dTVUKhW8vLy6PO/l5YXycv29mvLycr3llUolqqs1Ge+eeOIJvP/++5g8eTJEIhEGDRqE++67DytXruyxLklJSXByctI9AgICeixridQMg/IGTaBmu0cNADOGe0Eq4iO3srlPI24qNYOEb8+hRa7C+GBX/GVKaL8/M9TDHg+O8gUAnLhuntkTKVBbAVMsJLvdKH9nAMD5knqTfSbhltsTTjAM02sSCn3lOz9/+PBhfPDBB9iwYQPOnj2L77//Hv/73//w/vvv93jNxMRENDQ06B7FxcUDvR2zVNssh1ylhpDPM8q2zP5yshHhkdGaoPnvU4V3LL/pyA1kFNbBXiLEp4+PhoA/sDn25ycFAwAuljaY5SgfBWoLp2aYWz1qkwZqzfDU9cpmtMnNr2GQgXN3d4dAIOjWe66srOzWa9by9vbWW14oFMLNzQ0A8NZbbyEuLg6LFy/GyJEjMXfuXHz44YdISkqCWq1/r6xEIoGjo2OXhzXRzk97O0kHHOQM7Zl7gwBo5qmrm2U9ljtbVId/pF4DALz7yIi7+vs1NsAZHg4SKFQMLpY0DPg6bKHMZBauulmGdoUaIgHPZEfbAYC7vQSeDhJUNslwrZJWf1sTsViMyMhIpKamYu7cubrnU1NTMWfOHL3viY6OxoEDB7o8d/DgQURFRUEk0qS8bG1tBf+21LQCgQAMw9DJbT0o49Cwt9Yof2eM9ndC9s0GbD2er3dKrlWuxGeHrkOpZvDgKB88Ns6vT9fuKXMioNmRknq5AhdLGxAV7Drg+rOBetQWrrhW843az9nG5N+oh3lrjrejbVrWJyEhAVu2bMG2bduQk5OD+Ph4FBUVYenSpQA0Q9ILFy7UlV+6dCkKCwuRkJCAnJwcbNu2DVu3bsXrr7+uK/Pwww9j48aN2LNnD/Lz85Gamoq33noLjzzyCASC3rfqWCvt4ikuLCTr7NX7hwAAth3P79arlivV+PepQtS3KeBmJ0ZkoAt2ny7usjd8IEb4akZTblS2mN3wN/WoLRwb89Naw7wdcCy3GtcqmqBSM5wZeiPGt2DBAtTU1GDNmjUoKytDREQEUlJSEBSkGfYsKyvrsqc6JCQEKSkpiI+Px+effw5fX1+sX78ejz32mK7M6tWrwePxsHr1apSUlMDDwwMPP/wwPvjgA5Pfn7nQ9qh9OdSjBoBp4Z6YNNgNJ67XYM/pIiyOCYVUJEC7QoVdp4tQWNMKqYiPpyYEQioyzJcwTwcp3OzEqGmRI7+6BeE+5jMNQoHawpl6xXdnQa52kIr4aJWrcK64DpFB5jXcRO7OsmXLsGzZMr2v7dixo9tzU6dOxdmzZ3u8nlAoxDvvvIN33nnHUFW0aI3tCjTLlOAB8DLhtFdf8Hg8fDJ/NB7+13GUNrTjn7/nItTdDrmVzWiWKSES8PBsdLDBh+wHedijpqUWeVXNZhWoaejbgsmVat3WDDZ61AI+D0M8NcPff1yhc2gJMaWyjmFvdwcJxELu/an3c7bBvxeNh7OtCA1tCmQV16NZpoSbnRhLYkKNcoBIqIfmmnnVLQa/tjFRj9qC3axvBQPNlgg2zqAFgKFeDrhQ0oBjuVV4feYwVupAiDW6NezNrd50ZyN8nbB82hBcLm1EXascng5ShPk4GO08+xB3TaAua2hHq0wJ2z5mOGObedSSDIh2IVmAkdOG9mawpz0A4HxJA+pb5XqT6BNCDE+7kIxLK771kQgFGBvoYpLPcpCKdLtR8qpbEOHX9yxnbOLeeAgxGDYXkmk52WgaBsMAJ67XsFYPQqxNqbZH7cztQG1qgzw0nQdzGv42i0BtjOPy6uvr8fLLL8PHxwdSqRTh4eFISUkx1i2YHMMwXc6gZtOQjl71sVyapybEFNoVKtR2HMTjw+GhbzYEdpzJXdKx0NYccD5QG+O4PLlcjhkzZqCgoADfffcdrl69ii+//BJ+fn3bVG8OShva0SRTgs9j/xv1YF2grqbEFISYgHZ+2slG1OeTpqyFX8dUQFlDO1Rq8/h7xPmfYOfj8gDNwfG//vorNm7ciKSkpG7lOx+XBwDh4eHIyMjA2rVrdXsyt23bhtraWpw8eVKX9Ui7v9NSZBVpEt77ONmwvuIzxN0eIgEPJfVtyK9uQWjH0BMhxDjKdEdbUm/6dq72YkiEfMiUalQ19ZzClEs43aM21nF5P/74I6Kjo/Hyyy/Dy8sLERER+PDDD6FSmVe2mt5kFdUDAAJc2Z+fEgv5iOrYQ30s1zxPryHEnJTVcy91KFfweTzdKGOJmRx7yelAbazj8vLy8vDdd99BpVIhJSUFq1evxqefftprhiNzO9dW26Nme35aK2aoOwAK1ISYgvYwDq6lDuUK7ZY1CtQGZOjj8tRqNTw9PbF582ZERkbiiSeewKpVq7Bx48Yer2lO59rKlWpc7DgHms0V353FDPYAAKTdqIZCpf+kI0LI3VOq1ahs1Azpci11KFf4dWxZLaVAffeMdVyej48Phg4d2iWRf3h4OMrLyyGXy/Ve15zOtc0pa4RcqYaNSAA3O27sWx7h6wgXWxFa5CrdsDwhxPAqG2VQMQykIj6cbdlJdMR12qHvsoY2s1hQxulA3fm4vM5SU1MxceJEve+Jjo7uVv724/ImTZqE69evdznD9tq1a/Dx8YFYrD+wmdO5ttph70BX215HHkyJz+dh8hBNr/o4bdMixGhuLSSz4Uz75xp3ewlEAh4UKgYFNdzfT83pQA0Y57i8l156CTU1NVi+fDmuXbuGn376CR9++CFefvllk9+fMWQV1wPgxkKyzmIGa+apj9I8NSFGU2oGqUPZxufx4Omg+fe5XtnMcm3uzGjbs/Lz8xESEnLX1zHGcXkBAQE4ePAg4uPjMWrUKPj5+WH58uV444037rq+XHBOG6g5spBMa/IQTaA+f7MeDa0KONGwHOcYqt0S9mgP4/ChjGS98nCQoKS+DdcrmzFzBNu16Z3RAvXgwYMxZcoULFq0CPPnz4dUOvBvd4Y+Lg/QDJGfOnVqwHXiqppmGQprNBl3/DkWqH2dbTDY0x7XK5tx8kY1Zo30YbtK5DaGbLfE9NRqhrNnUO9K15+kii2eDhIAQOrlCrjoOYPgqQmBpq5Sj4w29J2dnY2xY8fir3/9K7y9vfHiiy/i9OnTxvo40kHbmx7saQ8bsWEOXDekyTT8zWnUbs1bcV0rZEo1hHwePDoCEdFPG6grm9pZrsmdGS1QR0REYN26dSgpKcH27dtRXl6OyZMnY8SIEVi3bh2qqmhBkTFoV1SPDXBmtR49maLbT11F6UQ5iNqtebvUsS3Ty1EKAZ8WkvVGO0dd1SSDmuN/i4y+mEwoFGLu3Ln49ttv8fHHH+PGjRt4/fXX4e/vj4ULF6KsrMzYVbAqWcWaFd+mOjauvyaEuEEk4OFmXZtuiJ5wD7Vb83SptAEApQ7tCxc7MQR8zcrvhlYF29XpldEDdUZGBpYtWwYfHx+sW7cOr7/+Om7cuIFDhw6hpKQEc+bMMXYVrIZKzSC7WNNQx3C0R20nEWJcx5cIOk2Lu6jdmqfLHT1qWkh2ZwI+D+72mrlprg9/Gy1Qr1u3DiNHjsTEiRNRWlqKnTt3orCwEH/7298QEhKCSZMm4Ysvvrjjoi/SdzeqmtEsU8JWLMBQL+4efDFlqGY/Nc1Tcw+1W/OmHfqmrVl949Ex/F3J8cM5jLbqe+PGjXjhhRfw/PPPw9vbW2+ZwMBAbN261VhVsDraRCej/J0gFHB3i3zMEHf8/derOHWjBgqVGiIO19XaULs1X1VNMlQ2ycAD4E2Buk+0C8q4foqW0QJ1amoqAgMDwed3/SPMMAyKi4sRGBgIsViMZ5991lhVsDq6hWQcnZ/WGuHrBBdbEepaFcgurkdUsCvbVSIdqN2ar8tlmt60m70YEiH3dnxwkTbFck2L/tTRXGG0rsygQYN0p1V1VltbSwkVjES7NYur89NaAj4Pkzq2af2WU8lybUhn1G7N162FZDQ/3Vdu9poeda21Buqett40NzdTEgUjaJYpcbWiCQB3t2Z19kCEZlj1f+dLaZsWhxiy3W7YsAEhISGQSqWIjIzEsWPHei1/5MgRREZGQiqVIjQ0FJs2bepWpr6+Hi+//DJ8fHwglUoRHh6OlJSUftXLUl2m+el+0/aoG9oUnD7Vz+BD3wkJCQA0R0q+/fbbsLW9lR1LpVIhPT0dY8aMMfTHWr3zxfVgGMDP2QaejtxvqNPCvGAnFuBmXRvOFtUjMojbw/WWztDtdu/evVixYgU2bNigW4A2a9YsXL58GYGB3TM+5efnY/bs2ViyZAm+/vprnDhxAsuWLYOHh4cu/a9cLseMGTPg6emJ7777Dv7+/iguLoaDg8Pd3byFoBXf/WcrFkAq4qNdoUZtixxeHP3bafBAnZWVBUDzzfzChQtdTqMSi8UYPXp0lwMyiGFoD+IYG+jMaj16oi994BAvB5wrrseB7FIK1CwzdLtdt24dFi1ahMWLFwMAkpOT8euvv2Ljxo1ISkrqVn7Tpk0IDAxEcnIyAM2xsxkZGVi7dq0uUG/btg21tbU4efKk7iQ8bc5/a9fUrkBeteYUKF8K1H3G4/HgaidGaX27dQXqP/74AwDw/PPP45///Cenj4O0JNoV31xfSNbZaH8nnCuux//OlyJxdhgtgGGRIdutXC5HZmYmVq5c2eX52NhYnDx5Uu970tLSEBsb2+W5mTNnYuvWrVAoFBCJRPjxxx8RHR2Nl19+GT/88AM8PDzw1FNP4Y033uhytnxnMpkMMtmtFb2NjY0Dvi8u67wty15itDXCFsnNToLS+nbUNHN35bfRfqLbt2831qXJbRiGMZuFZJ0N9nSAo1SI6mY5fjpfhnnj/A1y3d6S/3Mp0T4XGaLdVldXQ6VSwcvLq8vzXl5eKC8v1/ue8vJyveWVSiWqq6vh4+ODvLw8HDp0CE8//TRSUlKQm5uLl19+GUqlEm+//bbe6yYlJeG9996763viuoslmoVkEX5OLNfE/JjDym+DBup58+Zhx44dcHR0xLx583ot+/333xvyo63azbo2VDfLIRLwMMLXfEYwBHwe7g11w8HLFdh6PB9zx/rRQfcsMFa7vf1nyTBMrz9ffeU7P69Wq+Hp6YnNmzdDIBAgMjISpaWl+Pvf/95joE5MTNTNvwOaHnVAQECf78FcUKAeOLeO7GRcXvlt0EDt5OSka1ROTvQLYypnO4a9h/s6QSoyr+Hj8cGuOHKtCpdKG5GeX4t7Q93YrpLVMXS7dXd3h0Ag6NZ7rqys7NZr1vL29tZbXigUws1N8zvh4+MDkUjUZZg7PDwc5eXlkMvlXebVtSQSCSQSyz9F6kJHoB7p56Q75pL0jaud5vfDanrUnYfNaOjbdLh+YlZvbCVCzI/0xzfpRUhKycH+ZZPAp1N/TMrQ7VYsFiMyMhKpqamYO3eu7vnU1NQec4RHR0fjwIEDXZ47ePAgoqKidAvHJk2ahF27dkGtVusSsly7dg0+Pj56g7S1aJEpdQvJIihQ95u2R13fKodKzXDy1DGj7aNua2tDa+ut05EKCwuRnJyMgwcPGusjrdY5jq/4vpPl04fAXiJE9s0GfHf2Jmv12JVe1OPDWhiq3SYkJGDLli3Ytm0bcnJyEB8fj6KiIixduhSAZkh64cKFuvJLly5FYWEhEhISkJOTg23btmHr1q1dVpq/9NJLqKmpwfLly3Ht2jX89NNP+PDDD/Hyyy/f5V2bt8tljWAYwMtRQmdQD4CDRAiRgAc1o9lPzUVGW0w2Z84czJs3D0uXLkV9fT3Gjx8PsViM6upqrFu3Di+99JKxPtqqyJQq3f5Jc1pI1pmngxTLpw3BByk5+OCnHIwPdkWwu12v7+kcPBmGwY2qFlwsaUBdqxwCPg9ejlKMDXA2iz3lXGKodrtgwQLU1NRgzZo1KCsrQ0REBFJSUnTbqcrKylBUdOtnGBISgpSUFMTHx+Pzzz+Hr68v1q9fr9uaBQABAQE4ePAg4uPjMWrUKPj5+WH58uV44403DPuPYGYu3Lw17E36j8fjwdlGjKpmGepa5XC1497ojNEC9dmzZ/GPf/wDAPDdd9/B29sbWVlZ2LdvH95++20K1AZyrbwZcpUazrYiBLra3vkNHPXsxGD870IZsovr8cKOM/h2aTTc7XvvHciUKmQV1SMtr6ZbUv0r5U04cq0KI3wd8choXzhIRcasvsUwZLtdtmwZli1bpve1HTt2dHtu6tSpdzyVKzo6GqdOnepzHazBxVJaSHa3XOxEmkDdIgc82K5Nd0YL1K2trbqMQQcPHsS8efPA5/Nx7733orCw0Fgfa3XOl9QD0HybNucV02IhH18ujMSjn51AXnULHlp/HH//8yhMHuze7b5yK5rwv/OlyCysg0yp1r1/bIAz/F1soFAxuF7ZjJyyRlwqbUR+dQv+HOmPYd7msyKeLdRuzc/FEupR3y1nm455amsb+h48eDD++9//Yu7cufj1118RHx8PQLOSk5KgGI522GuUv/k3Uk8HKXYumoC//DsDeVUtiNt6GkO97BEZ5ApHqRA1LXKcK67H9cpm3Xvc7MSIHuSGcYEuXVa83xvqhrKGNnyXeRNlDe34Kq0Q94d5YsE9AZxcLMIV1G7NS6tcqWsP1KMeOBdbzYhbHUdXfhstUL/99tt46qmnEB8fj2nTpiE6OhqA5lv62LFjjfWxVufItSoAQH2rwiIWPQ32tMePr0zGJ79cwbcZxbhW0YxrFc1dyogEPAz2sMeEUDcM9rQHv4eRBB8nG7w0dRB+ulCG9PxaHLpSiRd2nME/nxgDZ1vuzUNxAbVb83L+ZgPUHQvJuJr+0hxo/x7UtVpZj3r+/PmYPHkyysrKMHr0aN3z06ZN67Jlgwxcu0KFikbNVgw/C8rvay8RYs2cCAS62iK3shkVje1QqhhIRHz4ONog2N0WtuK+/eoKBXzMGeOHQFdb/PdcCY5cq8JD/zqODU+Pwyh/Z+PeiBmidmtetDkUxplR6mAu0vao61utrEcNaJIYeHt7d3lu/PjxxvxIq5JT1gg1A9hJhHCysbzFUrZiIUYbKJiODXSBt5MUP5wrRVFtKx79/AT+HBmAv0wNxSAPe4N8hqWgdms+tDkUKFDfHeeOld6N7QpO7qU2WqBuaWnBRx99hN9//x2VlZVQq7ue9ZmXl2esj7Ya2mxE/s42Zr2QzFR8nGxw4NXJWP3fiziQXYq9GcXYm1GM8cGueHiML9rkKqs/0IDaLXt6mrrqKT89wzC6w3jGBTkbq1pWwV4ihIDPg0rNoLFNAReObdEy2l+lxYsX48iRI4iLi4OPjw8FEiM437GQzM/FvIe9TTm37mQjwr+eHIvnJgZh4+EbOHSlEqcLanG6oBZ8HhDm7Yjp4V7wdrLO+T5qt+ajuLZzjn9aSHY3+DwenG1EqGmRo65Vbj2B+ueff8ZPP/2ESZMmGesjrJ52xbclzU+bSmSQK7Y864qyhjb8dL4MP2aX4vzNBlwua0ROWSPuD/PEfWGePS5Us1TUbs2Hdn56hBnm+OciFzsxalrkqOfggjKjpRB1cXGBq6ursS5v9VrlSuRWNgGgQH03fJxssDgmFD++MhnLpw1BhK8jGAC/X6nEntNFUHec4GQtqN2aj8xC7Rn0zuxWxEI4d6zzqePggjKjBer3338fb7/9dpe8wcRwcsqaoGYAB6kQjha4kIwNXo5SPDUhCPPH+UPI5+FiaSN+Ol+mO27RGlC7NR9peTUAgAkhdOKcIWiHu7m4RctogfrTTz/Fr7/+Ci8vL4wcORLjxo3r8uiPDRs2ICQkBFKpFJGRkTh27Fiv5Y8cOYLIyEhIpVKEhoZi06ZNPZbds2cPeDweHn300X7ViW1XyjX5vX2sdC7VmMYFueDPUQHgQfPHMKvj0BNrYMh2S4ynqkmG65XN4PGAe0NpBMQQdElPONijNtoctaEC3969e7FixQps2LABkyZNwhdffIFZs2bh8uXLCAzsvhoyPz8fs2fPxpIlS/D111/jxIkTWLZsGTw8PLok+Ac0JwO9/vrriImJMUhdTelquWbYm5IcGMdIPydUhXvht5wK/HS+DG88EGYVJxOZ2xdWa3Wqozcd5u1IyXsMRJdG1JoC9TvvvGOQ66xbtw6LFi3C4sWLAQDJycn49ddfsXHjRiQlJXUrv2nTJgQGBiI5ORmA5mD5jIwMrF27tkugVqlUePrpp/Hee+/h2LFjqK+vN0h9jUHfquijHRnJvClQG83UoR64XNqA0oZ2rPnfZfzrScvPzGWodkuMSzvs7WorsoiMhFygHfpuaNPspeYSow19A0B9fT22bNmCxMRE1NbWAtCczlNSUtKn98vlcmRmZiI2NrbL87GxsTh58qTe96SlpXUrP3PmTGRkZEChuDX3sGbNGnh4eGDRokX9uSVOYBgG5R0Zyax1G5EpCPg8zB3nDx6AA9mluNRxSpGlu9t2S4xP26MOcadkPYbiIBVCwNOcS93Uzq15aqP1qM+fP4/p06fDyckJBQUFWLJkCVxdXbF//34UFhZi586dd7xGdXU1VCoVvLy8ujzv5eWF8vJyve8pLy/XW16pVKK6uho+Pj44ceIEtm7dinPnzvX5fmQyGWSyW0cpNjY29vm9htbYrkS7Qg0+D/C4w1GQ5O74OdtgpL8Tzt9swD9Sc7Hl2Si2q2RUhmi3xLhK6tuQV9UCHoCQO5zbTvqOz+PByVaE2hY55xaUGa1HnZCQgOeeew65ubmQSm/1+mbNmoWjR4/261q3J11gGKbXRAz6ymufb2pqwjPPPIMvv/wS7u7ufa5DUlISnJycdI+AgIB+3IFhlTe0AQDc7SUQCow6KEIATAvzAp8H/JZTodu7bqkM2W6JcfyeUwEACHSzhY2Y9k8bknaLFtfmqY32V/7MmTN48cUXuz3v5+fXY2/4du7u7hAIBN3KV1ZWdus1a3l7e+stLxQK4ebmhhs3bqCgoAAPP/wwhEIhhEIhdu7ciR9//BFCoRA3btzQe93ExEQ0NDToHsXFxX26B2Mob9T07GnY2zQ8HCR4ZLQvAGDbiXyWa2Nchmi3xLh+z6kEoFlIRgyLq6doGS1QS6VSvcPDV69ehYeHR5+uIRaLERkZidTU1C7Pp6amYuLEiXrfEx0d3a38wYMHERUVBZFIhLCwMFy4cAHnzp3TPR555BHcd999OHfuXI89ZYlEAkdHxy4PtmhPzKKFZKbz/KQQAMD/zpeiqkl2h9LmyxDtlhhPi0yJtBvaFd8OLNfG8jh3bNFqaONWj9poc9Rz5szBmjVr8O233wLQDDsXFRVh5cqV3bZJ9SYhIQFxcXGIiopCdHQ0Nm/ejKKiIixduhSApqdbUlKimztbunQpPvvsMyQkJGDJkiVIS0vD1q1bsXv3bgCaP0QRERFdPsPZ2RkAuj3PVeUNmkBNW7P6b6ArZEcHOGNMgDPOFddj9+kivDZtiIFrxg2GarfEOI7lVkOuUiPQ1RaeVrBd0NRuHXdpJT3qtWvXoqqqCp6enmhra8PUqVMxePBgODg44IMPPujzdRYsWIDk5GSsWbMGY8aMwdGjR5GSkoKgoCAAQFlZGYqKbv3xDQkJQUpKCg4fPowxY8bg/fffx/r16y3mj4xKzeh6dDT0bVrPTQwGAOw+XcS57RuGYqh2S4zjpwtlAIDp4V50YIoRONlwc+jbaD1qR0dHHD9+HH/88QcyMzOhVqsxbtw4TJ8+vd/XWrZsGZYtW6b3tR07dnR7burUqTh79myfr6/vGlxV1SyDimEgEfJ1Cx+I8e1KL4JCpYZUxEdZQzs++CkHgz01W2N6OobQHBmy3RLDamhT4NdLmnUCc8f66Y65JYbj0mno+06Llk3JKIFarVZjx44d+P7771FQUAAej4eQkBB4e3tz6ubNUedhb/p3NC2RgI9R/s44nV+Ls0V1ukBtKajdctv/zpdCrlRjmJcDIvwcKVAbgVNH50ehYlDbIocbR7a/Gnzom2EYPPLII1i8eDFKSkowcuRIjBgxAoWFhXjuuecwd+5cQ3+kValsovlpNo0LdAEAXCptgEyhYrk2hmOMdks5+g3ru8ybAID5kf70pclIhAI+HKSa/mtJfRvLtbnF4D3qHTt24OjRo/j9999x3333dXnt0KFDePTRR7Fz504sXLjQ0B9tFao75qetIe80FwW42MDdXoLqZhkulzVibEfgNneGbreUo9+wMgtrkVVUDyGfhzljfdmujkVzthGhqV2Jkro2jPJ3Zrs6AIzQo969ezfefPPNbo0dAO6//36sXLkS33zzjaE/1mpUNXcEao4MyVgbHo+HUf5OAICLFjT0aOh22zlHf3h4OJKTkxEQEICNGzfqLd85R394eDgWL16MF154AWvXru1SrnOO/tDQ0P7dpBlb//t1AJretKcDjaYZk3YvNZd61AYP1OfPn8cDDzzQ4+uzZs1Cdna2oT/WKqgZBtXNmv191KNmT4SfJlDnVjaj3UKGvw3ZbilHv2FlFdXhyLUqCPg8LPvTYLarY/G0e6m5FKgNPvRdW1vbY9YwQJN3u66uztAfaxXqWuRQqRkI+TzdLxMxPS8HCTzsJahqluFKeVOve7PNZUW4Idst5eg3HJWawer/XgSgWekd6GbLco0sn65HXcedQG3wHrVKpYJQ2HP8FwgEUCqVhv5Yq1DdMeztbi8BnxaTsIbH4yHCT5OZzlKGv43RbilH/907cq0Kl0ob4Wwrwv89MIzt6lgFFxsr6FEzDIPnnnsOEon+odnO325J/2gTnbjTsDfrIvyc8MfVKlyraIJMoYJEZN6HIxiy3RojR/+lS5d0Ofq11Go1AEAoFOLq1asYNGhQt+smJiYiISFB9/+NjY1mE6yvVTTh0BXNARzvPTKC5qZNxMkahr6fffbZO5ahFd8DQwvJuMPbUQo3OzFqWuS4UtGE0RxZHTpQhmy3nXP0d97WlZqaijlz5uh9T3R0NA4cONDlOX05+jtbvXo1mpqa8M9//rPXHP09ffngsrKGNuw6XQQ1Azw2zl93KAwxPpeOoe/6VgVaZErYSYyWF6zPDF6D7du3G/qSpEOVbmuWmOWaEM3wtxOOXKtCTlmj2QdqQ7dbytE/cA1tCnx1sgBypRqhHnZImjeS9k2bkFQkgFTER7tCjdL6NgzxYv/wE/a/KpA+q9Ku+LanITAuCPd2wJFrmuFvlZqBgE9/TLUWLFiAmpoarFmzBmVlZYiIiOhTjv74+Hh8/vnn8PX1tagc/X2lUKmxM60Aje1KeDpI8PT4IIiFdOa8qTnbiFGuaMdNCtSkP1rlSrTINIt53KlHzQn+rrawFQvQKlehsLYFoe6WlVL0blGO/v47kF2KsoZ22IkFeHZiMGzE5r32wVw524pQ3tjOmZXf9FXNTGgzkjnZiCARUuPlAj6Ph2Ed37avljWxXBti7i6WNCCjsA48AAvuCdTNlRLT025/LeXIgjIK1GZCO+ztbk+Nl0vCfDTbtK6UU6AmA9euUCGl4wjLKUM9LO7AF3PjbMOt7GQUqM1EFeX45qQhnvbg8zQr8muaaeshGZgvj+ahvk0BJxsR7hvmyXZ1rJ4uOxkNfZP+oK1Z3CQVCRDsbgeAetVkYBrbFfjiaB4A4IER3rR4jAO4lu+bfiPMxK0eNa345powb83w91UK1GQA/pNxE80yzSpv7YEvhF3aHnVFYzsUKjXLtaFAbRZUaga1Ldr0oTRHzTVDvTTziQU1LZAr2W/UxHyo1Ax2nMwHAEwc5E77pTnCXiKEWMCHmgHKG9rZrg4FanNQ2yKHmgHEAj4cbegwDq7xsJfAyUYEpZpBQU0L29UhZuTQlUoU17bB2VaEMQHObFeHdODzePB11oxecmH4mwK1GbiV41tMh3FwEI/H063SvV7ZzHJtiDnZn3UTAPB4VADNTXOMn4sNAG4sKKPfDDNQ1enULMJNQyhQk35qalfgt5xKAMCjY/xYrg25na9TR6CmHjXpi2ramsV5gzzswQNQ3tiOxnYF29UhZuDXSxWQK9UY4mmPcB/201SSrrQ9ai4kPaFAbQZoaxb32UmE8HXWNOwb1KsmffDDuRIAwCOjfWkRGQf5OVOPmvQRwzCU7MRMaOepcylQkztoaFXg5I0aAMDDdIQlJ9EcNemzmhY52hQq8EBz1FzXeZ6aYRiWa0O47PC1SqjUDIZ62esS5hBu6dyjZrs9U6DmuLwqzXYfZ1sRRAL6cXFZoJstRAIemmVKVDZROlHSM+0ismnhXizXhPTEx8kGPB4gU6pR0yJntS70l5/jblRphlFp2Jv7hHw+glw1vaP8atpPTfRTqNQ4fFUTqKeHU15vrhIL+fDs+LvL9vA3BWqO0y5MooVk5iHEQxOo86ponprod6agFk3tSrjaiTEmwIXt6pBecGVBGQVqjtP2qN2pR20WQjvmG/OqW1if1yLcdORaFQDgT8M8IODTam8u0+7koB51H2zYsAEhISGQSqWIjIzEsWPHei1/5MgRREZGQiqVIjQ0FJs2bery+pdffomYmBi4uLjAxcUF06dPx+nTp415CwOW1zGESj1q8+DnYgORgIdWuYrmqYlex3OrAQBThniwXBNyJ7qV39Sj7t3evXuxYsUKrFq1CllZWYiJicGsWbNQVFSkt3x+fj5mz56NmJgYZGVl4c0338Rrr72Gffv26cocPnwYTz75JP744w+kpaUhMDAQsbGxKCkpMdVt9Um7QoWi2lYANEdtLjrPU+fRPDW5TU2zDJdKGwEAkwa7s1wbcif+HBn6FrL66X2wbt06LFq0CIsXLwYAJCcn49dff8XGjRuRlJTUrfymTZsQGBiI5ORkAEB4eDgyMjKwdu1aPPbYYwCAb775pst7vvzyS3z33Xf4/fffsXDhQuPeUD/cqGoGwwA2IgHsJZz/UZEOoR52uF7VjHyapya3OdGxdzrM26HPX753pevvlBDj83exBQAUd3SY2MLpHrVcLkdmZiZiY2O7PB8bG4uTJ0/qfU9aWlq38jNnzkRGRgYUCv2pHVtbW6FQKODq6mqYihuINm+0p6OEMheZkRCapyY9ONEx7D2ZetNmIdBNE6iLaltZbcucDtTV1dVQqVTw8uq619DLywvl5eV631NeXq63vFKpRHV1td73rFy5En5+fpg+fXqPdZHJZGhsbOzyMLbcio5A7SA1+mcRw+k8T32tgnrVRINhGBy/3hGoh1CgNgf+Ljbg84BWuUqXypkNZjGeentvkmGYXnuY+srrex4APvnkE+zevRuHDx+GVNpzQExKSsJ7773Xn2rftdzKJgDQ7eUj5kHI5yPIzQ7XK5txKq8Gw7zpwAVro2+4urpJhpL6NogFfEwIcWOhVqS/JEIBfJ1tcLOuDYU1rax1mjjdo3Z3d4dAIOjWe66srOzWa9by9vbWW14oFMLNrWvjWLt2LT788EMcPHgQo0aN6rUuiYmJaGho0D2Ki4sHcEf9k9tp6JuYF+02rVN5NSzXhD3WvFtDn9yONQuRQS6wEQtYrg3pq6CO4e8CFheHcjpQi8ViREZGIjU1tcvzqampmDhxot73REdHdyt/8OBBREVFQSQS6Z77+9//jvfffx+//PILoqKi7lgXiUQCR0fHLg9jkivVKKzRLGCgoW/z0zlQq9XWN09tzbs1eqJdc0LD3uYlyE3TlotYXFDG6UANAAkJCdiyZQu2bduGnJwcxMfHo6ioCEuXLgWg6el2Xqm9dOlSFBYWIiEhATk5Odi2bRu2bt2K119/XVfmk08+werVq7Ft2zYEBwejvLwc5eXlaG7mznxiQU0LVGoGDhIhHKVmMUNBOvFz0eT9rmtVWOVpWp13a4SHhyM5ORkBAQHYuHGj3vKdd2uEh4dj8eLFeOGFF7B27VpdmW+++QbLli3DmDFjEBYWhi+//BJqtRq///67qW5rwFRqRpetjhaSmZdgbY+6hgJ1jxYsWIDk5GSsWbMGY8aMwdGjR5GSkoKgoCAAQFlZWZdv6SEhIUhJScHhw4cxZswYvP/++1i/fr1uaxagGZKTy+WYP38+fHx8dI/OfxTYpl1INtjLnlZ8myEBn4dgN+sc/rb23Rr63KxrhUyphpONCBF+TmxXh/SDtkddWMPe0LdZdNWWLVuGZcuW6X1tx44d3Z6bOnUqzp492+P1CgoKDFQz49EuJNMenUjMT4i7HXI7FpQ9OzGY7eqYjDF2a/j4+HR7T193a8hkt1brmmK3hj7XO3rTkwa7UdpQM0Nz1KRH2uHSIZ60YthcafdTp+fXWuU8tSl2a3z//fd33K3h5OSkewQEBPTnFgxGNz89mNKGmptAV02gbmxXor6VneMuKVBz1PVOQ9/EPPm52MBGJEBti9yq5qmtfbfG7WQKlS6zFc1Pmx9bsRBeHTtv2Dq+lgI1BylVauRVa3vUFKjNlZDPR1Sw5hjD9Hzrmae25t0a+tyoaoGaAVztxLpMV8S8hLpr/g7fqKJATToU1LRAoWJgKxbA18mG7eqQu3BvqKY3aG0Lyqx1t4Y+tN7E/A3pGNnU/ixNzSwWk1kb7ek6Yd4O4NPCE7N2b6hmRfKpvNo7ztFakgULFqCmpgZr1qxBWVkZIiIi+rRbIz4+Hp9//jl8fX173a3R2TvvvIN3333XJPc1ENppj6FeDnTAhpnSfsm6zlJKYArUHKQN1CN8aRuHuRvp59xlnnqol/UsDrTG3Rq3q2mWobZFDj7vVhIcYn4GdyzqZWutCQ19c9BlXaA2/XwaMSyx8NY8tbUNfxPgWscf9iA3O0hElDbUXGmHvovrWtEmV5n88ylQcwzDMLhU2gCAetSWwlrnqQmQW6GZ0xxK89Nmzd1eAlc7MRgGuMHCOfMUqDmmrKEdda0KCPg83bc4Yt5un6cm1kGpViOvYzvPECua8rBUg7Xz1CwMf1Og5hjt/PQQT3tIaajMIoz0c4ZUxLe6/dTWrqimFXKlGnYSIbyd6GAdc6ddUMbGym8K1ByjnZ8eTvPTFkMs5CMqSNurpuFva3Gl/NawN99KVvtbMm2gvsbCym8K1ByTfbMeABBB89MWRTv8nXaDArU1YBgGl8s0X7rDfehLtyXQ/hwvlTSY/LMpUHOIWs3gbFEdAM3h8sRyTOpIHXniejWUKjXLtSHGVtmk2ZYlpLUmFmOEnxN4PKC0oR1VTbI7v8GAKFBzSF51M+pbFZCK+DT0bWFG+TvD2VaExnYlzhXXs10dYmQ5Hb3pQR72kAhprYklsJcIMchD86XrQkm9ST+bAjWHZBRoetOj/Z0hEtCPxpII+DzEDNGcnHTkWhXLtSHGpl0USsPelmWUv2ZK8vxN0w5/UzTgkMxCTaDWJsgglmXqUArU1qCgugUl9W3gAQj3oW1ZlmSUHwVqq6cN1DQ/bZmmDNXMU5+/2YDqZtPOcRHT+TG7FIBm362DVHSH0sScjApwBqBpw6bMiUCBmiOqmmS65AjjAilQWyJPBylGdnwj/+1yBcu1IcbAMAx+OFcCQDOFRSzLcB9HCPk8VDfLUNbQbrLPpUDNEUc7hkNH+DrC2VbMcm2IsTwQ4Q0ASLlYznJNiDFcKm3EjaoWCPk8WhBqgaQigW7dwen8WpN9LgVqjjh0tRIAcH+YJ8s1IcY0qyNQn7xejfpWOcu1IYa2+7TmGMtwH0fKLGihJg7W5O4/mmu6tSYUqDlAqVLretT3UaC2aKEe9gjzdoBSzSCVhr8tSlO7Av/N0gx7jw9xZbk2xFimdOzeOJ5bbbJ5agrUHJBZWIemdiVcbEU0r2UFZkX4ALi16IhYhv9mlaBFrsIgDzs6e9qCRQa5QCLko7JJZrJ0ohSoOeDQFc2w99ShHhDwKSewpZs71g8AcPx6NYprW1muDTEElZrB9pMFAIBn7g0Cj3J7WyypSIAJHUfXHjPR8DcFapap1QwOdPSsYkd4s1wbYgqBbraIGeIOhgH2nCliuzrEAP53vhR5VS1wshFhfqQ/29UhRjZliGar5R8da4uMjQI1y07l1aC0oR0OUiEtJLMiT40PBAB8m3ETciXl/jZnKjWD9b/nAgCWxITQ3mkrMGO4FwDNITuVTcbfpkWBmmU70woBAHPG+NIqUSsyfbgXPBwkqGqS4bvMm2xXh9yFvWeKcaOjN/3sxGC2q0NMIMjNDmMCnKFmgB/PGX+tCQVqFhVUt+DgZc1+2oXRwexWhpiUSMDHS1MHAQD+dSgX7QoVyzUiA1HZ2I6kn3MAAMunDaHetBXRTnF8k14Etdq4q78pULPon7/nQs0A9w3zwFAvyglsbZ6aEAgfJynKGtqxM62A7eqQflKrGaz670U0tSsxyt+JetNWZu5YPzhIhMjv1OEyFgrULDmdX4v9HXsu42cMZbk2hA1SkQDx0zU/+08PXsPV8iaWa0T6Y/2hXKReroBIwMOHc0fSjg0rYycR4rlJwQA07deYa03MIlBv2LABISEhkEqliIyMxLFjx3otf+TIEURGRkIqlSI0NBSbNm3qVmbfvn0YPnw4JBIJhg8fjv379xur+t3UtsgRv/ccAODJ8QEYRXunrdafo/zxp2EekCnVeHX3WTS0KtiuksFYWrvtbMuxPCT/pllA9sHckYjoyOFOrMvimFC42omRW9mMzw7lGu1zOB+o9+7dixUrVmDVqlXIyspCTEwMZs2ahaIi/dta8vPzMXv2bMTExCArKwtvvvkmXnvtNezbt09XJi0tDQsWLEBcXByys7MRFxeHxx9/HOnp6Ua/n8qmdizclo6S+jYEudnizdnhRv9Mwl08Hg9/nz8a7vYSXKtoxpNfnjLJKlJjs7R2q9XUrsCb+y/gbz9p5qVf+tMgPB4VYLLPJ9ziZCPCu4+MAACsP3Qd+7OMszCUx5jyrK4BmDBhAsaNG4eNGzfqngsPD8ejjz6KpKSkbuXfeOMN/Pjjj8jJydE9t3TpUmRnZyMtLQ0AsGDBAjQ2NuLnn3/WlXnggQfg4uKC3bt396lejY2NcHJyQkNDAxwd75x8v6ldge/PlmBd6jU0tCngaifGty/ei8Ged56b3pVOe23N1VMTAvtULqesEXFb01HdLIejVIjl04di/jh/ONneeXFSf38XTcFS2q1WeUM79meVYMfJfFQ0ao4ofeOBMCydGqo3uQm1WfPX17YLAO/+eAk7OhLe/DnSH3+NHQZvJ2mv7+nP76KwzzVhgVwuR2ZmJlauXNnl+djYWJw8eVLve9LS0hAbG9vluZkzZ2Lr1q1QKBQQiURIS0tDfHx8tzLJyckGqffFkgZkFdejplmGqiYZciubca64XjeHEebtgI3PRCKE0gySDuE+jvj2xWi8ujsLl0ob8f7/LuPjn69ghJ8jwrwd4eskxbggF0wa7M52Ve/IXNvt9cpmZBfXo1mmRFO7Ag1tChTWtOJ6VTPyqlp05QJdbZE0b6RZ/CyIabz90HCIhXx8eSwP/8m8ie/O3sSYAGeM8HVEdKg7Hhzlc1fX53Sgrq6uhkqlgpeXV5fnvby8UF6uf5VdeXm53vJKpRLV1dXw8fHpsUxP1wQAmUwGmUym+/+GhgYAmm9Ft/vx9HVsOprX7fkQd1s8NSEQf44MgFCg0vtefVpbaJGRuerrzxgA3CXA1wtH4bvMYuw9U4xrFc3IzG1GZq5mn+afo/wx0rP7Eajaz+DK4Ji5ttufz+bj779e03sdHg8Y6eeE+ZH+mD3SB1KRoNefLbVZ89eftgsAr0z2w8T/3979x0Rd/3EAf55w3B0XXgPjexGoUCxcaUuwpALsD6VoDm2rKFOo5UKlZrQ1WZbcyoP64zbLUuMYGOX6ZSnYL+47FJbLMrfSAUMNnaYl+wZDwJmd9/r+YXzovJOADu/D5/N8bGx8Pp/353Pv1+fu9Xnd53Ofu3eSBRv+exQHTvTgwJFzOHDkNLr+9x9kTQ88KRtN3qq6UA+6/NKSiAz7W7rB2l8+f7TbrKiogMPhCJiflDTyz6dOAmgBUDziNWiiWx7Cbbn++ruSvr4+2GzqualJK3k76ASAz0e9Fk1UocrdKgBVw2xsJHmr6kI9ZcoUREREBLxj7urqCnhnPchutwdtHxkZibi4uGHbXGmbAFBWVobS0lJl2ufzobu7G3FxcSH5Af6zZ88iKSkJJ0+eVM3njONNbzGPV7wigr6+PiQkJIRsm/+GnvI2GL29rseDHvbhaPJW1YU6KioK6enp8Hg8WLx4sTLf4/EgPz8/6DqZmZloaGjwm9fY2IiMjAwYjUaljcfj8fu8q7GxEXfdddcV+2IymWAymfzmXXvttaMN6R9NnjxZsy/MK9FbzOMRr5rOpPWYt8Ho7XU9HrS+D0ect6JyH3zwgRiNRqmurpa2tjZZvXq1WK1WOX78uIiIrFmzRpYuXaq07+zslOjoaHnuueekra1NqqurxWg0yieffKK02bt3r0REREhlZaW0t7dLZWWlREZGyr59+656fIN6e3sFgPT29oatD1eb3mLWU7x6ydtg9PQ8jxfuQ3+qL9QiIm+99ZZMmzZNoqKiZPbs2dLc3KwsKywslJycHL/2e/bskdtvv12ioqJk+vTpsmnTpoBtfvzxx3LzzTeL0WiUtLQ02b59+3iHMSw9vjD1FrPe4tVD3gajt+d5PHAf+lP996j14o8//kBFRQXKysoCLtVpld5i1lu8esXn+d/jPvTHQk1ERKRiqv8JUSIiIj1joSYiIlIxFmoiIiIVY6FWgdEOBziRtLS0YOHChUhISIDBYMCOHTv8losIysvLkZCQAIvFgnnz5qG1tTU8nQ2BiooKzJkzBzExMYiPj8eiRYvQ0dHh10ZrMZM/LedzqOnt+DBWLNRhNtrhACeagYEB3Hbbbdi4cWPQ5a+//jpcLhc2btyI/fv3w263Y/78+ejrm5i/ldzc3IxVq1Zh37598Hg88Hq9WLBgAQYGhgZ10FrMNETr+Rxqejs+jFkYvxpGInLHHXdIcXGx37y0tDRZs2ZNmHo0fgDIZ599pkz7fD6x2+1SWVmpzDt//rzYbDbZvHlzGHoYel1dXQJA+Q6xHmLWMz3lc6jp8fgwUjyjDqPB4QAvH95vuOEAteTYsWP47bff/OI3mUzIycnRTPyDozXFxsYC0EfMeqX3fA415soQFuowGstwgFoyGKNW4xcRlJaW4p577sGtt94KQPsx65ne8znUmCtDVD0oh16Mdug+rdFq/CUlJTh48CC++eabgGVajZn43IYa9yfPqMNqLMMBaondbgcATcb/zDPPoL6+Hrt370ZiYqIyX8sx653e8znUmCtDWKjD6O/DAf6dx+MZdug+rUhOTobdbveL/8KFC2hubp6w8YsISkpK8Omnn6KpqQnJycl+y7UYM12i93wONebKEF76DrPS0lIsXboUGRkZyMzMxDvvvIMTJ06guLg43F0Lif7+fhw9elSZPnbsGH788UfExsZi6tSpWL16NZxOJ1JTU5Gamgqn04no6Gg89thjYez12K1atQrbtm3Dzp07ERMTo5wN2Gw2WCwWGAwGzcVMQ7Sez6Gmt+PDmIXxjnP6y3DDAU50u3fvFgABf4WFhSJy6SsY69atE7vdLiaTSbKzs+XQoUPh7fS/ECxWAFJTU6O00VrM5E/L+Rxqejs+jBVHzyIiIlIxfkZNRESkYizUREREKsZCTUREpGIs1ERERCrGQk1ERKRiLNREREQqxkJNRESkYizUREREKsZCTUREpGIs1BSgqKgIBoMBBoMBkZGRmDp1KlasWIGenp5wd42ISHdYqCmo++67D7/++iuOHz8Ot9uNhoYGrFy5MtzdIiLSHRZqCspkMsFutyMxMRELFizAI488gsbGRmV5TU0NZsyYAbPZjLS0NLz99tt+6//yyy8oKChAbGwsrFYrMjIy8N133ynLGxoakJ6eDrPZjJSUFDgcDni9XmW5wWCA2+3G4sWLER0djdTUVNTX1/s9RmtrKx544AFMnjwZMTExyMrKws8//4yWlhYYjcaAcWyff/55ZGdnh3I3ERGNOxZq+kednZ346quvYDQaAQBVVVV48cUXsX79erS3t8PpdOKll17C1q1bAVwaui4nJwenT59GfX09fvrpJ7zwwgvw+XwAgK+//hqPP/44nn32WbS1tWHLli2ora3F+vXr/R7X4XDg4YcfxsGDB5GXl4clS5agu7sbAHDq1ClkZ2fDbDajqakJBw4cwJNPPgmv14vs7GykpKSgrq5O2ZbX68V7772HJ5544mrsMiKi0An38F2kPoWFhRIRESFWq1XMZrMy9JzL5RIRkaSkJNm2bZvfOq+88opkZmaKiMiWLVskJiZGfv/996Dbz8rKEqfT6Tevrq5Orr/+emUagKxdu1aZ7u/vF4PBIF9++aWIiJSVlUlycrJcuHAh6GO89tprMmPGDGV6x44dcs0110h/f/9IdwMRkSpwmEsKUFRUhFOnTmHTpk04d+4c3G43Dh8+jF27dqGnpwfx8fGwWCyYNGnogozX64XNZsOZM2ewcuVKtLa2orm5Oej2rVYrfD4fIiIilHkXL17E+fPnMTAwgOjoaBgMBnz00Ud46KGHlDY2mw1vvvkmli1bhry8PFx33XXKWfzlurq6kJiYiJaWFsydOxf5+fmYMmUKqqurQ7SXiIiujshwd4DUyWq14qabbgIAvPHGG7j33nvhcDhQUlIC4NLl7zvvvNNvncHCa7FYht22z+eDw+HAgw8+GLDMbDYr/w9eah9kMBiUy+f/9Bjx8fFYuHAhampqkJKSgi+++AJ79uwZdh0iIjVioaYRWbduHe6//36sWLECN9xwAzo7O7FkyZKgbWfNmgW3243u7m7ExsYGLJ89ezY6OjqUNwJjMWvWLGzduhV//vlnQEEf9NRTT6GgoACJiYm48cYbcffdd4/58YiIwoU3k9GIzJs3D7fccgucTifKy8tRUVGBDRs24PDhwzh06BBqamrgcrkAAI8++ijsdjsWLVqEvXv3orOzE9u3b8e3334LAHj55Zfx7rvvory8HK2trWhvb8eHH36ItWvXjrg/JSUlOHv2LAoKCvDDDz/gyJEjqKurQ0dHh9ImNzcXNpsNr776Km8iI6IJi4WaRqy0tBRVVVXIzc2F2+1GbW0tZs6ciZycHNTW1iI5ORkAEBUVhcbGRsTHxyMvLw8zZ85EZWWlcmk8NzcXu3btgsfjwZw5czB37ly4XC5MmzZtxH2Ji4tDU1OTcod5eno6qqqq/M6uJ02ahKKiIly8eBHLli0L7c4gIrpKeDMZadry5ctx5syZgO9gExFNFPyMmjSpt7cX+/fvx/vvv4+dO3eGuztERGPGQk2alJ+fj++//x5PP/005s+fH+7uEBGNGS99ExERqRhvJiMiIlIxFmoiIiIVY6EmIiJSMRZqIiIiFWOhJiIiUjEWaiIiIhVjoSYiIlIxFmoiIiIVY6EmIiJSsf8DPSULDyk3eF4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.93\n",
      "-0.72\n",
      "0.32\n",
      "-0.1\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "def analyze_skewness(x):\n",
    "    fig, ax = plt.subplots(2, 2, figsize=(5,5))\n",
    "    sns.distplot(customers[x], ax=ax[0,0])\n",
    "    sns.distplot(np.log(customers[x]), ax=ax[0,1])\n",
    "    sns.distplot(np.sqrt(customers[x]), ax=ax[1,0])\n",
    "    sns.distplot(stats.boxcox(customers[x])[0], ax=ax[1,1])\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    print(customers[x].skew().round(2))\n",
    "    print(np.log(customers[x]).skew().round(2))\n",
    "    print(np.sqrt(customers[x]).skew().round(2))\n",
    "    print(pd.Series(stats.boxcox(customers[x])[0]).skew().round(2))\n",
    "analyze_skewness('Recency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1196d445-42cd-4165-a8ff-8c10b84e7d77",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\24211\\AppData\\Local\\Temp\\ipykernel_24268\\2182937455.py:4: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(customers[x], ax=ax[0,0])\n",
      "C:\\Users\\24211\\AppData\\Local\\Temp\\ipykernel_24268\\2182937455.py:5: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(np.log(customers[x]), ax=ax[0,1])\n",
      "C:\\Users\\24211\\AppData\\Local\\Temp\\ipykernel_24268\\2182937455.py:6: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(np.sqrt(customers[x]), ax=ax[1,0])\n",
      "C:\\Users\\24211\\AppData\\Local\\Temp\\ipykernel_24268\\2182937455.py:7: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(stats.boxcox(customers[x])[0], ax=ax[1,1])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.77\n",
      "0.85\n",
      "3.67\n",
      "0.16\n"
     ]
    }
   ],
   "source": [
    "analyze_skewness('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "729103d0-ff6c-4a2f-ab06-d225072f5666",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\24211\\AppData\\Local\\Temp\\ipykernel_24268\\1584298847.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(customers['MonetaryValue'], ax=ax[0])\n",
      "C:\\Users\\24211\\AppData\\Local\\Temp\\ipykernel_24268\\1584298847.py:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(np.cbrt(customers['MonetaryValue']), ax=ax[1])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.63\n",
      "1.16\n"
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(10,3))\n",
    "sns.distplot(customers['MonetaryValue'], ax=ax[0])\n",
    "sns.distplot(np.cbrt(customers['MonetaryValue']), ax=ax[1])\n",
    "plt.show()\n",
    "print(customers['MonetaryValue'].skew().round(2))\n",
    "print(np.cbrt(customers['MonetaryValue']).skew().round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9cdf110c-1c5e-450a-bc3a-6a00ec9669d5",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2685</th>\n",
       "      <td>7.832068</td>\n",
       "      <td>0.591193</td>\n",
       "      <td>3.408514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2686</th>\n",
       "      <td>1.269495</td>\n",
       "      <td>1.435599</td>\n",
       "      <td>5.907565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2687</th>\n",
       "      <td>4.288385</td>\n",
       "      <td>0.591193</td>\n",
       "      <td>-1.669108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2688</th>\n",
       "      <td>1.665555</td>\n",
       "      <td>1.615329</td>\n",
       "      <td>4.273206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2689</th>\n",
       "      <td>6.340700</td>\n",
       "      <td>1.017445</td>\n",
       "      <td>4.087250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Recency  Frequency  MonetaryValue\n",
       "2685  7.832068   0.591193       3.408514\n",
       "2686  1.269495   1.435599       5.907565\n",
       "2687  4.288385   0.591193      -1.669108\n",
       "2688  1.665555   1.615329       4.273206\n",
       "2689  6.340700   1.017445       4.087250"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "customers_fix = pd.DataFrame()\n",
    "customers_fix[\"Recency\"] = stats.boxcox(customers['Recency'])[0]\n",
    "customers_fix[\"Frequency\"] = stats.boxcox(customers['Frequency'])[0]\n",
    "customers_fix[\"MonetaryValue\"] = pd.Series(np.cbrt(customers['MonetaryValue'])).values\n",
    "customers_fix.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0de39de2-c496-4041-acd3-6a0b0e05feb3",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0. -0.  0.]\n",
      "[1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "# 导入库 \n",
    "from sklearn.preprocessing import StandardScaler \n",
    "# 初始化对象 \n",
    "scaler = StandardScaler() \n",
    "# 拟合和转换数据 \n",
    "scaler.fit(customers_fix)\n",
    "customers_normalized = scaler.transform(customers_fix) \n",
    "# 均值为 0，方差为 1 \n",
    "print(customers_normalized.mean(axis = 0).round(2)) # [0. -0。 0.] \n",
    "print(customers_normalized.std(axis = 0).round(2)) # [1. 1. 1.]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "93414c05-2713-4594-8f27-3c465933cfeb",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=11.\n",
      "  warnings.warn(\n",
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=11.\n",
      "  warnings.warn(\n",
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=11.\n",
      "  warnings.warn(\n",
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=11.\n",
      "  warnings.warn(\n",
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=11.\n",
      "  warnings.warn(\n",
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=11.\n",
      "  warnings.warn(\n",
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=11.\n",
      "  warnings.warn(\n",
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=11.\n",
      "  warnings.warn(\n",
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=11.\n",
      "  warnings.warn(\n",
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=11.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "sse = {}\n",
    "for k in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    kmeans.fit(customers_normalized)\n",
    "    sse[k] = kmeans.inertia_\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('SSE')\n",
    "sns.pointplot(x=list(sse.keys()), y=list(sse.values()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b1c10bfc-c9c5-40df-ab3d-175eaae72cca",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "F:\\Users\\24211\\anaconda3\\envs\\machine_learning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=11.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2690,)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = KMeans(n_clusters=3, random_state=42)\n",
    "model.fit(customers_normalized)\n",
    "model.labels_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f4f2f0b8-825e-40e8-807d-e6619e2217fc",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th colspan=\"2\" halign=\"left\">MonetaryValue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46.25</td>\n",
       "      <td>7.71</td>\n",
       "      <td>165.79</td>\n",
       "      <td>924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47.62</td>\n",
       "      <td>1.61</td>\n",
       "      <td>21.51</td>\n",
       "      <td>811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>227.29</td>\n",
       "      <td>1.64</td>\n",
       "      <td>28.70</td>\n",
       "      <td>955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Recency Frequency MonetaryValue      \n",
       "           mean      mean          mean count\n",
       "Cluster                                      \n",
       "0         46.25      7.71        165.79   924\n",
       "1         47.62      1.61         21.51   811\n",
       "2        227.29      1.64         28.70   955"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers[\"Cluster\"] = model.labels_\n",
    "customers.groupby('Cluster').agg({\n",
    "    'Recency':'mean',\n",
    "    'Frequency':'mean',\n",
    "    'MonetaryValue':['mean', 'count']}).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5816aba4-4c66-4fc1-a9ed-733c1515b118",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
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